-------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\Jonathan A. Solis\Documents\Toshiba\Prosp\Killing_Msng\FP
> A\FPA_replication\Solis_fpa_Stata.smcl
  log type:  smcl
 opened on:  31 May 2019, 15:16:17

. 
. *Load Data
. clear 

. cd "C:\Users\Jonathan A. Solis\Documents\Toshiba\Prosp\Killing_Msng\FPA\FPA_r
> eplication"
C:\Users\Jonathan A. Solis\Documents\Toshiba\Prosp\Killing_Msng\FPA\FPA_replica
> tion

. use Solis_fpa_may2019

. set more off

. 
. *Prepare dataset for analysis*
. 
. *sort 
. sort country year

. 
. *n. log regime durability and other variables
. gen seq_ln=ln(seq1)
(527 missing values generated)

. gen gdppc_ln=ln(gdppc)
(481 missing values generated)

. gen gdp_ln=ln(gdp)
(478 missing values generated)

. gen fdi_ln=ln(fdi)
(677 missing values generated)

. 
. *create dummy years (will need separate variables for rare events logit/figur
> es)
. drop y1* y2*

. forvalues i=1992/2016{
  2. 
. gen y`i'=1 if year==`i'
  3. replace y`i'=0 if y`i'==.
  4. }
(4,086 missing values generated)
(4,086 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,085 missing values generated)
(4,085 real changes made)
(4,085 missing values generated)
(4,085 real changes made)

. *
. 
. *Create binary journalist killed variable
. gen logit=1 if confirmed!=0 & confirmed!=.
(3,813 missing values generated)

. replace logit=0 if logit==.
(3,813 real changes made)

. 
. *Create journalist killed ordinal variables
.   
. *Ordinal 1 (not killed, one killed, 2 or more killed)
.   gen ord1=.
(4,252 missing values generated)

.   replace ord1=0 if conf ==0 
(3,813 real changes made)

.   replace ord1=1 if conf==1
(236 real changes made)

.   replace ord1=2 if conf>=2
(203 real changes made)

. 
.  *Ordinal 2 (not killed, 1-9 killed, 10 or more killed)
.   gen ord2=.
(4,252 missing values generated)

.   replace ord2=0 if conf ==0
(3,813 real changes made)

.   replace ord2=1 if inrange(conf,1,9)
(421 real changes made)

.   replace ord2=2 if conf>=10
(18 real changes made)

.   
. *-----------------------*
. ************************* 
. ***MANUSCRIPT ANALYSIS***
. ************************* 
. *-----------------------*
. 
. *********  
. *Table 1*
. *********
.   
. *With original Asal et al. (2018) indicators; 1992-2011*
.   
. *Model 1: Logit
. logit logit seq_ln polity qog physint speech intensity2 info pop_ln ///
>   y199* y2*, cluster(ccode) nolog
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Logistic regression                             Number of obs     =      2,491
                                                Wald chi2(27)     =     401.52
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -584.14814               Pseudo R2         =     0.3199

                                (Std. Err. adjusted for 132 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
       logit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.2988275   .1016697    -2.94   0.003    -.4980964   -.0995586
     polity2 |   .0787478    .023658     3.33   0.001     .0323791    .1251166
         qog |  -2.431464   .9997759    -2.43   0.015    -4.390989   -.4719396
     physint |  -.4442775   .0808364    -5.50   0.000     -.602714    -.285841
      speech |   .3731656   .1692742     2.20   0.027     .0413942     .704937
  intensity2 |   .8238264    .159725     5.16   0.000     .5107711    1.136882
        info |   .0349404   .0080296     4.35   0.000     .0192027    .0506781
      pop_ln |   .4031775   .1006037     4.01   0.000     .2059978    .6003572
       y1992 |    .201045   .4923028     0.41   0.683    -.7638508    1.165941
       y1993 |   .7010179   .4753637     1.47   0.140    -.2306779    1.632714
       y1994 |   .4293873   .4797246     0.90   0.371    -.5108557     1.36963
       y1995 |   .1727027   .5209772     0.33   0.740     -.848394    1.193799
       y1996 |   .1905449    .551811     0.35   0.730    -.8909848    1.272075
       y1997 |   .0322176   .4972464     0.06   0.948    -.9423674    1.006803
       y1998 |   .2265051   .5205984     0.44   0.664     -.793849    1.246859
       y1999 |  -.7130851   .5703336    -1.25   0.211    -1.830918    .4047482
       y2000 |  -.1490753   .4508462    -0.33   0.741    -1.032718    .7345671
       y2001 |   .4684062   .4587789     1.02   0.307    -.4307839    1.367596
       y2002 |  -.6645619   .4805198    -1.38   0.167    -1.606364    .2772397
       y2003 |  -.2783981   .4197885    -0.66   0.507    -1.101169    .5443723
       y2004 |   .2605484   .3746932     0.70   0.487    -.4738367    .9949335
       y2005 |  -.0754616   .4316625    -0.17   0.861    -.9215046    .7705815
       y2006 |  -.5952437   .4358473    -1.37   0.172    -1.449489    .2590013
       y2007 |  -.2479877   .4526166    -0.55   0.584      -1.1351    .6391245
       y2008 |  -.8299853   .4119472    -2.01   0.044    -1.637387   -.0225837
       y2009 |   .1215153   .3959572     0.31   0.759    -.6545465    .8975771
       y2010 |   .0277551   .3894038     0.07   0.943    -.7354625    .7909726
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |   -8.18878   1.732541    -4.73   0.000     -11.5845   -4.793062
------------------------------------------------------------------------------

. 
. *Store estimates
. est sto m1

. 
. *Percentage of zeros? (how many country-years without a journalist killed)
. predict resid
(option pr assumed; Pr(logit))
(1,761 missing values generated)

. replace resid=. if year>=2012
(0 real changes made)

. sum resid

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       resid |      2,491    .1091931    .1724382    .000387   .9505066

. gen missing1=.
(4,252 missing values generated)

. replace missing1 = 1 if resid!=.
(2,491 real changes made)

. replace missing1 = 0 if resid==.
(1,761 real changes made)

. tab missing1

   missing1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,761       41.42       41.42
          1 |      2,491       58.58      100.00
------------+-----------------------------------
      Total |      4,252      100.00

. *about 89% 0's
. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
          m1 |      2,491  -858.959  -584.1481      28    1224.296   1387.269
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1224.296
. 
. *Model 2: Rare Events Logit
. relogit logit seq_ln polity qog physint speech intensity2 info pop_ln ///
>   y1992 y1993 y1994 y1995 y1996 y1997 y1998 y1999 y2000 y2001 ///
>   y2002 y2003 y2004 y2005 y2006 y2007 y2008 y2009 y2010, cluster(ccode) 
(1,761 missing values generated)


Corrected logit estimates                             Number of obs =     2491

------------------------------------------------------------------------------
             |               Robust
       logit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.2917181   .1005395    -2.90   0.004     -.488772   -.0946642
     polity2 |   .0761276    .023395     3.25   0.001     .0302743     .121981
         qog |  -2.359823   .9886627    -2.39   0.017    -4.297566   -.4220799
     physint |  -.4343086   .0799379    -5.43   0.000    -.5909839   -.2776332
      speech |   .3637731   .1673926     2.17   0.030     .0356896    .6918566
  intensity2 |    .797144   .1579496     5.05   0.000     .4875685    1.106719
        info |   .0339439   .0079403     4.27   0.000     .0183811    .0495066
      pop_ln |   .3937834   .0994854     3.96   0.000     .1987955    .5887712
       y1992 |   .2105377   .4868305     0.43   0.665    -.7436326    1.164708
       y1993 |   .6892535   .4700797     1.47   0.143    -.2320859    1.610593
       y1994 |   .4309252   .4743921     0.91   0.364    -.4988663    1.360717
       y1995 |   .1847493   .5151862     0.36   0.720    -.8249971    1.194496
       y1996 |   .1996103   .5456772     0.37   0.715    -.8698975    1.269118
       y1997 |   .0448176   .4917191     0.09   0.927    -.9189343    1.008569
       y1998 |   .2288744   .5148116     0.44   0.657    -.7801378    1.237887
       y1999 |  -.6764494   .5639939    -1.20   0.230    -1.781857    .4289583
       y2000 |  -.1396486   .4458348    -0.31   0.754    -1.013469    .7341715
       y2001 |   .4571669   .4536793     1.01   0.314    -.4320281    1.346362
       y2002 |  -.6331324   .4751785    -1.33   0.183    -1.564465    .2982004
       y2003 |  -.2643571   .4151223    -0.64   0.524    -1.077982    .5492676
       y2004 |   .2568161   .3705282     0.69   0.488    -.4694059     .983038
       y2005 |  -.0694985   .4268643    -0.16   0.871    -.9061372    .7671402
       y2006 |  -.5724358   .4310026    -1.33   0.184    -1.417185    .2723137
       y2007 |  -.2364397   .4475854    -0.53   0.597    -1.113691    .6408117
       y2008 |  -.7967247   .4073681    -1.96   0.050    -1.595151    .0017021
       y2009 |   .1201524   .3915558     0.31   0.759    -.6472829    .8875877
       y2010 |    .028018   .3850754     0.07   0.942    -.7267158    .7827519
       _cons |  -7.987803   1.713283    -4.66   0.000    -11.34578    -4.62983
------------------------------------------------------------------------------

. 
. *Store estimates
. est sto m2

. 
. *Unable to get AIC from RE Logit model
. 
. *Model 3: Ordinal 1
. ologit ord1 seq_ln polity qog physint speech intensity2 info pop_ln ///
>   y199* y2*, cluster(ccode) nolog
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Ordered logistic regression                     Number of obs     =      2,491
                                                Wald chi2(27)     =     523.05
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -752.27166               Pseudo R2         =     0.2796

                                (Std. Err. adjusted for 132 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
        ord1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3460709   .1047118    -3.30   0.001    -.5513022   -.1408396
     polity2 |   .0766976   .0242441     3.16   0.002     .0291799    .1242152
         qog |  -2.597162   1.035908    -2.51   0.012    -4.627504   -.5668189
     physint |  -.4421272   .0873438    -5.06   0.000    -.6133179   -.2709365
      speech |   .3879509   .1664538     2.33   0.020     .0617074    .7141945
  intensity2 |   .8714902    .177043     4.92   0.000     .5244924    1.218488
        info |   .0383628    .008331     4.60   0.000     .0220344    .0546912
      pop_ln |   .4045641    .103532     3.91   0.000      .201645    .6074831
       y1992 |   .3365865   .4857364     0.69   0.488    -.6154393    1.288612
       y1993 |   .8410613    .545497     1.54   0.123    -.2280931    1.910216
       y1994 |   .4937923   .5326878     0.93   0.354    -.5502566    1.537841
       y1995 |   .2374511   .5788913     0.41   0.682     -.897155    1.372057
       y1996 |   .1520628   .5871383     0.26   0.796    -.9987072    1.302833
       y1997 |   .1455187    .525548     0.28   0.782    -.8845364    1.175574
       y1998 |    .364688   .5573421     0.65   0.513    -.7276824    1.457058
       y1999 |  -.5538598   .6153306    -0.90   0.368    -1.759886     .652166
       y2000 |  -.1785531   .4500227    -0.40   0.692    -1.060581    .7034752
       y2001 |   .3898908   .4830897     0.81   0.420    -.5569477    1.336729
       y2002 |  -.5362592   .4991315    -1.07   0.283    -1.514539    .4420206
       y2003 |  -.2590127   .4167701    -0.62   0.534    -1.075867    .5578416
       y2004 |   .3004279   .3588667     0.84   0.403    -.4029379    1.003794
       y2005 |   .0561238   .4119925     0.14   0.892    -.7513667    .8636142
       y2006 |  -.5639247   .4241244    -1.33   0.184    -1.395193    .2673438
       y2007 |  -.3300982   .4295083    -0.77   0.442    -1.171919    .5117226
       y2008 |  -.5735658   .4512848    -1.27   0.204    -1.458068    .3109361
       y2009 |   .0558304   .3637326     0.15   0.878    -.6570725    .7687333
       y2010 |   .0852993   .3729961     0.23   0.819    -.6457596    .8163581
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
-------------+----------------------------------------------------------------
       /cut1 |   8.263341   1.827126                      4.682239    11.84444
       /cut2 |   9.562313   1.802868                      6.028757    13.09587
------------------------------------------------------------------------------

. 
. *Store estimates
. est sto m3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
          m3 |      2,491 -1044.239  -752.2717      29    1562.543   1731.336
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1562.543
. 
. *Model 4: Ordinal 2
. ologit ord2 seq_ln polity qog physint speech intensity2 info pop_ln ///
>   y199* y2*, cluster(ccode) nolog
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Ordered logistic regression                     Number of obs     =      2,491
                                                Wald chi2(27)     =     402.50
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -600.39546               Pseudo R2         =     0.3142

                                (Std. Err. adjusted for 132 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
        ord2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3040945   .1003299    -3.03   0.002    -.5007374   -.1074516
     polity2 |   .0763017   .0238368     3.20   0.001     .0295825     .123021
         qog |  -2.381014   .9727136    -2.45   0.014    -4.287498   -.4745304
     physint |  -.4431923   .0805603    -5.50   0.000    -.6010876   -.2852971
      speech |   .3933047   .1747027     2.25   0.024     .0508936    .7357157
  intensity2 |   .8325259   .1579066     5.27   0.000     .5230347    1.142017
        info |   .0343692   .0076631     4.49   0.000     .0193498    .0493886
      pop_ln |   .3977182   .0980266     4.06   0.000     .2055896    .5898468
       y1992 |   .1759834   .4832318     0.36   0.716    -.7711335      1.1231
       y1993 |   .6747289   .4632242     1.46   0.145    -.2331739    1.582632
       y1994 |   .4350852   .4827863     0.90   0.367    -.5111585    1.381329
       y1995 |   .2455144   .5302813     0.46   0.643    -.7938179    1.284847
       y1996 |   .1613657   .5397944     0.30   0.765     -.896612    1.219343
       y1997 |   .0112429   .4925138     0.02   0.982    -.9540664    .9765522
       y1998 |   .2123576   .5137227     0.41   0.679    -.7945204    1.219236
       y1999 |  -.7297122   .5674601    -1.29   0.198    -1.841914    .3824891
       y2000 |  -.1649399   .4445969    -0.37   0.711    -1.036334     .706454
       y2001 |   .4473866   .4520526     0.99   0.322    -.4386202    1.333393
       y2002 |  -.6696363   .4766375    -1.40   0.160    -1.603829     .264556
       y2003 |  -.2792677   .4153613    -0.67   0.501    -1.093361    .5348255
       y2004 |   .2545123   .3698555     0.69   0.491    -.4703912    .9794158
       y2005 |  -.0725427   .4262688    -0.17   0.865    -.9080142    .7629287
       y2006 |  -.5948293   .4310626    -1.38   0.168    -1.439696    .2500378
       y2007 |  -.2489001   .4481335    -0.56   0.579    -1.127226    .6294255
       y2008 |  -.8193351   .4099442    -2.00   0.046    -1.622811   -.0158593
       y2009 |   .1947757   .3931653     0.50   0.620    -.5758141    .9653655
       y2010 |   .0321199   .3858092     0.08   0.934    -.7240524    .7882921
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
-------------+----------------------------------------------------------------
       /cut1 |   8.092871   1.684435                      4.791438     11.3943
       /cut2 |   13.47501   1.922687                      9.706608     17.2434
------------------------------------------------------------------------------

. 
. *Store estimates
. est sto m4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
          m4 |      2,491  -875.464  -600.3955      29    1258.791   1427.584
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1258.791
.   
. *Model 5: NBREG
. nbreg confirmed seq_ln polity qog physint speech intensity2 info pop_ln ///
>   y199* y2*, cluster(ccode) nolog
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      2,491
                                                Wald chi2(27)     =     652.80
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -943.86262               Pseudo R2         =     0.2419

                                (Std. Err. adjusted for 132 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4147484   .0886796    -4.68   0.000    -.5885572   -.2409396
     polity2 |   .0484784   .0216734     2.24   0.025     .0059993    .0909575
         qog |  -1.834329   .7834535    -2.34   0.019     -3.36987   -.2987887
     physint |  -.3937754   .0646856    -6.09   0.000    -.5205569   -.2669939
      speech |   .3202279   .1378818     2.32   0.020     .0499845    .5904713
  intensity2 |    .876815   .1422519     6.16   0.000     .5980064    1.155624
        info |   .0294872   .0054437     5.42   0.000     .0188179    .0401566
      pop_ln |   .3734167   .0827939     4.51   0.000     .2111437    .5356897
       y1992 |   .0884891   .4060512     0.22   0.827    -.7073566    .8843348
       y1993 |   .8340258   .3866615     2.16   0.031     .0761832    1.591869
       y1994 |    .794954   .4398802     1.81   0.071    -.0671953    1.657103
       y1995 |   .4014312   .3763249     1.07   0.286    -.3361521    1.139014
       y1996 |   .1088092   .4287766     0.25   0.800    -.7315776     .949196
       y1997 |  -.1042736   .3892987    -0.27   0.789    -.8672851    .6587379
       y1998 |   .1382009   .3809713     0.36   0.717    -.6084891    .8848909
       y1999 |  -.2775612   .4658704    -0.60   0.551     -1.19065    .6355281
       y2000 |   -.484316   .3881937    -1.25   0.212    -1.245162    .2765296
       y2001 |   .2913317   .4203177     0.69   0.488    -.5324758    1.115139
       y2002 |  -.4113312   .4289074    -0.96   0.338    -1.251974    .4293118
       y2003 |  -.2323672   .3438186    -0.68   0.499    -.9062392    .4415048
       y2004 |   .4325644   .3175331     1.36   0.173    -.1897891    1.054918
       y2005 |  -.0623076   .3061763    -0.20   0.839    -.6624022    .5377869
       y2006 |  -.7569267   .3087687    -2.45   0.014    -1.362102   -.1517512
       y2007 |   -.392278   .3046626    -1.29   0.198    -.9894057    .2048498
       y2008 |  -.5855509   .3301148    -1.77   0.076    -1.232564    .0614623
       y2009 |   .1510743   .3298872     0.46   0.647    -.4954928    .7976414
       y2010 |   .1591018   .3479981     0.46   0.648    -.5229619    .8411656
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -7.341823   1.349772    -5.44   0.000    -9.987328   -4.696319
-------------+----------------------------------------------------------------
    /lnalpha |   .5258509   .2633299                      .0097337    1.041968
-------------+----------------------------------------------------------------
       alpha |   1.691898   .4455273                      1.009781    2.834791
------------------------------------------------------------------------------

. 
. *Store estimates
. est sto m5

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
          m5 |      2,491 -1244.965  -943.8626      29    1945.725   2114.518
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1945.725
. 
. *Model 6: ZINB
. zinb confirmed seq_ln polity qog physint speech intensity2 info pop_ln ///
>   y199* y2*, inflate(pop_ln) cluster(ccode) nolog
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Zero-inflated negative binomial regression      Number of obs     =      2,491
                                                Nonzero obs       =        272
                                                Zero obs          =      2,219

Inflation model      = logit                    Wald chi2(27)     =     512.70
Log pseudolikelihood =  -943.592                Prob > chi2       =     0.0000

                                (Std. Err. adjusted for 132 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
confirmed    |
      seq_ln |  -.4088585   .0861488    -4.75   0.000    -.5777069     -.24001
     polity2 |   .0487761   .0215682     2.26   0.024     .0065031    .0910491
         qog |  -1.837069   .7796545    -2.36   0.018    -3.365164   -.3089747
     physint |  -.3930197   .0644458    -6.10   0.000    -.5193311   -.2667083
      speech |     .32709   .1396083     2.34   0.019     .0534628    .6007171
  intensity2 |   .8689017   .1409008     6.17   0.000     .5927412    1.145062
        info |   .0292112   .0053013     5.51   0.000     .0188208    .0396016
      pop_ln |   .3392241   .1163912     2.91   0.004     .1111014    .5673468
       y1992 |   .0979655   .4073954     0.24   0.810    -.7005149    .8964459
       y1993 |   .8475523    .400022     2.12   0.034     .0635236    1.631581
       y1994 |   .8133544   .4618724     1.76   0.078    -.0918988    1.718608
       y1995 |    .414588   .3870385     1.07   0.284    -.3439936    1.173169
       y1996 |   .1208742   .4401866     0.27   0.784    -.7418757    .9836241
       y1997 |  -.0799811   .4127239    -0.19   0.846    -.8889052    .7289429
       y1998 |   .1447522   .3824097     0.38   0.705    -.6047571    .8942615
       y1999 |  -.2757365   .4631593    -0.60   0.552    -1.183512    .6320391
       y2000 |  -.4807708   .3860981    -1.25   0.213    -1.237509    .2759677
       y2001 |   .2893862   .4206141     0.69   0.491    -.5350024    1.113775
       y2002 |  -.4054189   .4233519    -0.96   0.338    -1.235173    .4243355
       y2003 |  -.2239395   .3412441    -0.66   0.512    -.8927657    .4448867
       y2004 |   .4331325   .3144358     1.38   0.168    -.1831503    1.049415
       y2005 |  -.0641614   .3008296    -0.21   0.831    -.6537766    .5254538
       y2006 |  -.7414992   .3128561    -2.37   0.018    -1.354686   -.1283124
       y2007 |  -.3893312   .2999859    -1.30   0.194    -.9772928    .1986304
       y2008 |  -.5629627   .3501409    -1.61   0.108    -1.249226    .1233009
       y2009 |   .1607408   .3311973     0.49   0.627     -.488394    .8098757
       y2010 |   .1641934   .3448998     0.48   0.634    -.5117978    .8401847
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -6.720305   2.037459    -3.30   0.001    -10.71365   -2.726959
-------------+----------------------------------------------------------------
inflate      |
      pop_ln |  -1.013648   1.030984    -0.98   0.326    -3.034339    1.007043
       _cons |   13.48712   13.77712     0.98   0.328    -13.51553    40.48978
-------------+----------------------------------------------------------------
    /lnalpha |   .4897171   .2851715     1.72   0.086    -.0692088    1.048643
-------------+----------------------------------------------------------------
       alpha |   1.631854   .4653584                      .9331318    2.853776
------------------------------------------------------------------------------

. 
. *Store estimates
. est sto m6

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
          m6 |      2,491 -1141.887   -943.592      31    1949.184   2129.618
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1949.184
. 
. ****Create TABLE 1 for LaTex (basic table; I make some changes by hand once g
> enerated)****
. esttab m1 m2 m3 m4 m5 m6 using table1.tex, replace se aic obslast r2 ///
> mtitle("Logit" "RE Logit" "Ordinal 1" "Ordinal 2" "NBREG" "ZINB" ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" qog ///
> "Quality of Govt." physint "Physical Integrity" speech "Freedom of Speech" //
> /
>  intensity2 "Armed Conflict" info "Information Flows" pop_ln "Population (ln)
> ") ///
>  varwidth(2) scalar(N_g) drop(y1* y2* _cons) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(output written to table1.tex)

. 
. *Generate Fig. 2
. 
. *run model 1 w/ year variables
. quietly nbreg confir seq_ln polity qog physint speech intensity2 info pop_ln 
> ///
>   y1992 y1993 y1994 y1995 y1996 y1997 y1998 y1999 y2000 y2001 ///
>   y2002 y2003 y2004 y2005 y2006 y2007 y2008 y2009 y2010, cluster(ccode)

. 
. *create figure
. prgen seq_ln, from (0) to (5.5) generate(durat) rest(mean) ci 

nbreg: Predicted values as seq_ln varies from 0 to 5.5.

        seq_ln     polity2         qog     physint      speech  intensity2     
>    info      pop_ln       y1992       y1993       y1994       y1995
x=   2.7904289   3.8875953    .5586714   4.7330389   1.0264954   .22159775   58
> .437234   16.373106   .04456042   .04576475   .04536331    .0461662

         y1996       y1997       y1998       y1999       y2000       y2001     
>   y2002       y2003       y2004       y2005       y2006       y2007
x=   .04656764    .0461662   .04696909   .05098354   .05098354   .05258932   .0
> 5178643   .05218788   .05178643   .05258932   .05218788   .05258932

         y2008       y2009       y2010
x=   .05258932   .05218788   .05299077

. label variable duratp1 "Probability of Journalist Killing" 

. label variable duratx "Regime Duration"  

. label variable duratp1lb "95% lower limit" 

. label variable duratp1ub "95% upper limit"

. 
. twoway (connected duratp1 duratx,lpattern(solid) lwidth(thin) msymbol(none) /
> //
> yaxis(1)) (connected duratp1lb duratx, lpattern(dash) lwidth(thin) msymbol(no
> ne) ///
> yaxis(1)) (connected  duratp1ub duratx, lpattern(dash) lwidth(thin) msymbol(n
> one) yaxis(1) ///
> ylab(,nogrid))

. 
. *Note: I further stylize figure for manuscript
.   
. *********
. *Table 2*
. *********
. 
. *Expanded models w/ new variables (1992-2014)
. 
. *NBREG
. 
. *Model 7: All regime types (worldwide)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln ///
>   y199* y2*, cluster(ccode) nolog 
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      3,586
                                                Wald chi2(30)     =     661.88
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood =  -1398.192               Pseudo R2         =     0.2294

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3106351   .0834917    -3.72   0.000    -.4742759   -.1469944
     polity2 |   .0088907   .0262709     0.34   0.735    -.0425992    .0603807
  public_cor |    .561591   .5537222     1.01   0.310    -.5236846    1.646867
 physical_vd |  -4.802629   .7428907    -6.46   0.000    -6.258668    -3.34659
  express_vd |   3.498511   .7174112     4.88   0.000     2.092411    4.904611
  intensity2 |   1.324489   .1304699    10.15   0.000     1.068772    1.580205
        info |   .0293556    .005941     4.94   0.000     .0177115    .0409998
      pop_ln |   .3936279   .0723554     5.44   0.000     .2518139     .535442
       y1992 |   .0276937   .4167372     0.07   0.947    -.7890962    .8444836
       y1993 |   .5913903   .3903377     1.52   0.130    -.1736575    1.356438
       y1994 |   .6669282   .4030154     1.65   0.098    -.1229676    1.456824
       y1995 |   .2040026   .3553771     0.57   0.566    -.4925236    .9005289
       y1996 |  -.4404533    .424516    -1.04   0.299    -1.272489    .3915828
       y1997 |  -.3087765   .4269718    -0.72   0.470    -1.145626    .5280728
       y1998 |  -.2725075   .3674019    -0.74   0.458     -.992602    .4475871
       y1999 |  -.2881471   .5009288    -0.58   0.565    -1.269949    .6936552
       y2000 |  -.3490986   .4155011    -0.84   0.401    -1.163466    .4652686
       y2001 |   .1182037   .4049446     0.29   0.770    -.6754732    .9118806
       y2002 |  -.7228458   .4108431    -1.76   0.079    -1.528083    .0823919
       y2003 |  -.1277249   .3665382    -0.35   0.727    -.8461266    .5906768
       y2004 |   .2004927    .377902     0.53   0.596    -.5401816    .9411671
       y2005 |  -.1493786   .3821905    -0.39   0.696    -.8984582     .599701
       y2006 |  -.6884493   .4133903    -1.67   0.096    -1.498679    .1217808
       y2007 |  -.0707424   .3326728    -0.21   0.832    -.7227692    .5812844
       y2008 |  -.5125084   .4048739    -1.27   0.206    -1.306047    .2810298
       y2009 |   .0373324    .397979     0.09   0.925    -.7426921    .8173569
       y2010 |   .0766111   .4166422     0.18   0.854    -.7399926    .8932149
       y2011 |  -.1179033   .3072662    -0.38   0.701     -.720134    .4843273
       y2012 |   .2042015   .2608626     0.78   0.434    -.3070797    .7154828
       y2013 |   .1070991   .3104499     0.34   0.730    -.5013716    .7155698
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.615959   1.257846    -7.64   0.000    -12.08129   -7.150626
-------------+----------------------------------------------------------------
    /lnalpha |   .7697525   .2160568                      .3462889    1.193216
-------------+----------------------------------------------------------------
       alpha |   2.159232   .4665167                      1.413811     3.29767
------------------------------------------------------------------------------

. 
. *Identify observation in full sample (will need for appendix analysis) 
. gen byte full=e(sample)

. 
. *Store estimates
. est sto t2m1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        t2m1 |      3,586 -1814.508  -1398.192      32    2860.384   3058.297
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=2860.384 
. 
. *IRR=.7329813 (seq_ln)
. 
. ************************************
. *Incidence Rate Ratio (IRR) Results*
. ************************************
. 
. *Add the 'irr' after comma to reproduce Incidence Rate Ratio (IRR) results. F
> or example,
. * for model 7:
. *
. *nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 i
> nfo pop_ln ///
> *  y199* y2*, irr cluster(ccode) nolog 
. *IRR=.7329813 (seq_ln)
. 
. *Model 8: Only autocracies
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln ///
>   y199* y2* if durable2==0, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =        597
                                                Wald chi2(30)     =   18028.60
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -177.54622               Pseudo R2         =     0.2611

                                 (Std. Err. adjusted for 52 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4229192   .2065462    -2.05   0.041    -.8277423   -.0180962
     polity2 |    .052775   .2046526     0.26   0.797    -.3483367    .4538867
  public_cor |  -1.193993   1.167744    -1.02   0.307     -3.48273    1.094743
 physical_vd |   -3.62313   1.446106    -2.51   0.012    -6.457446   -.7888138
  express_vd |   2.074716   1.734317     1.20   0.232    -1.324484    5.473915
  intensity2 |   2.056332   .3216458     6.39   0.000     1.425918    2.686746
        info |   .0383621   .0133249     2.88   0.004     .0122459    .0644783
      pop_ln |   .2319736   .1340842     1.73   0.084    -.0308266    .4947738
       y1992 |  -.1694253   .9150166    -0.19   0.853    -1.962825    1.623974
       y1993 |   .4093471    .881268     0.46   0.642    -1.317906    2.136601
       y1994 |   .6983873   .7636414     0.91   0.360    -.7983224    2.195097
       y1995 |  -1.002525   1.072872    -0.93   0.350    -3.105316    1.100266
       y1996 |  -1.222106   1.365937    -0.89   0.371    -3.899294    1.455082
       y1997 |  -.5836361   .9761521    -0.60   0.550    -2.496859    1.329587
       y1998 |  -1.385013     .92845    -1.49   0.136    -3.204741    .4347159
       y1999 |   .1988496   1.109434     0.18   0.858    -1.975601      2.3733
       y2000 |  -1.401388   1.423752    -0.98   0.325     -4.19189    1.389114
       y2001 |  -.6289573   1.670176    -0.38   0.706    -3.902442    2.644527
       y2002 |  -2.691142   .9327192    -2.89   0.004    -4.519238   -.8630462
       y2003 |  -.4979877   1.235528    -0.40   0.687    -2.919578    1.923602
       y2004 |  -.7102823   1.474936    -0.48   0.630    -3.601104    2.180539
       y2005 |  -.3598646   1.181977    -0.30   0.761    -2.676497    1.956768
       y2006 |   .3280802    1.10428     0.30   0.766    -1.836269    2.492429
       y2007 |   .5968205   1.352419     0.44   0.659    -2.053873    3.247514
       y2008 |  -20.62263   .5613413   -36.74   0.000    -21.72284   -19.52242
       y2009 |   .4497999   1.058855     0.42   0.671    -1.625518    2.525118
       y2010 |  -.6055939   1.608711    -0.38   0.707     -3.75861    2.547422
       y2011 |   .3462765    .829526     0.42   0.676    -1.279565    1.972118
       y2012 |   .5696525   .2429637     2.34   0.019     .0934524    1.045853
       y2013 |   .4255086   .0810152     5.25   0.000     .2667217    .5842956
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -5.856612   2.697189    -2.17   0.030    -11.14301   -.5702185
-------------+----------------------------------------------------------------
    /lnalpha |   1.170426   .5354017                      .1210585    2.219794
-------------+----------------------------------------------------------------
       alpha |   3.223367   1.725796                      1.128691    9.205439
------------------------------------------------------------------------------

. 
. *Identify observation in autocracies sample (will need for appendix analysis)
>  
. gen byte auto=e(sample)

. 
. *Store estimates
. est sto t2m2

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        t2m2 |        597 -240.2823  -177.5462      32    419.0924   559.6338
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=419.0924
. 
. *IRR=.6551315 (seq_ln)
. 
. *Model 9: Only anocracies
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln ///
>   y199* y2* if durable2==1, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,067
                                                Wald chi2(30)     =     440.60
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -487.84291               Pseudo R2         =     0.2252

                                 (Std. Err. adjusted for 86 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.5887558   .1394777    -4.22   0.000    -.8621271   -.3153844
     polity2 |   .0323224   .0383261     0.84   0.399    -.0427954    .1074402
  public_cor |   .3491755   .7289131     0.48   0.632    -1.079468    1.777819
 physical_vd |  -4.280589   1.070863    -4.00   0.000    -6.379441   -2.181737
  express_vd |    3.77605   .8828427     4.28   0.000      2.04571     5.50639
  intensity2 |   1.243058   .1540148     8.07   0.000     .9411948    1.544922
        info |   .0309397   .0078844     3.92   0.000     .0154867    .0463928
      pop_ln |   .2561858   .1041984     2.46   0.014     .0519607     .460411
       y1992 |    .195307   .5521017     0.35   0.724    -.8867925    1.277406
       y1993 |   .8402319   .4367644     1.92   0.054    -.0158107    1.696274
       y1994 |   1.062201   .4760914     2.23   0.026     .1290789    1.995323
       y1995 |   .9386339   .4609941     2.04   0.042      .035102    1.842166
       y1996 |   .1001989   .5847327     0.17   0.864    -1.045856    1.246254
       y1997 |  -1.336875   .6107057    -2.19   0.029    -2.533836   -.1399135
       y1998 |   .0203368    .580541     0.04   0.972    -1.117503    1.158176
       y1999 |   .2359288   .5746411     0.41   0.681    -.8903472    1.362205
       y2000 |   .1264427   .5696399     0.22   0.824     -.990031    1.242916
       y2001 |  -.2555609   .7952827    -0.32   0.748    -1.814286    1.303165
       y2002 |   -.985417   .7016024    -1.40   0.160    -2.360532    .3896983
       y2003 |  -.0283426   .5706569    -0.05   0.960     -1.14681    1.090124
       y2004 |   .2501701   .6836098     0.37   0.714    -1.089681    1.590021
       y2005 |   .6467149   .5630362     1.15   0.251    -.4568159    1.750246
       y2006 |  -.5110928   .5464731    -0.94   0.350    -1.582161    .5599748
       y2007 |   .2842678   .5214772     0.55   0.586    -.7378088    1.306344
       y2008 |   .0057168    .517079     0.01   0.991    -1.007739    1.019173
       y2009 |   .3727246   .5021858     0.74   0.458    -.6115416    1.356991
       y2010 |   .6517274    .601625     1.08   0.279    -.5274359    1.830891
       y2011 |   .2053959   .3890636     0.53   0.598    -.5571547    .9679466
       y2012 |    .189976   .4713913     0.40   0.687     -.733934    1.113886
       y2013 |   .8121666   .4894834     1.66   0.097    -.1472033    1.771536
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -7.557138   1.483786    -5.09   0.000     -10.4653   -4.648971
-------------+----------------------------------------------------------------
    /lnalpha |   .4984617   .2721675                     -.0349767      1.0319
-------------+----------------------------------------------------------------
       alpha |   1.646187   .4480385                      .9656279    2.806393
------------------------------------------------------------------------------

. 
. *Identify observation in anocracies sample (will need for appendix analysis) 
. gen byte ano=e(sample)

. 
. *Store estimates
. est sto t2m3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        t2m3 |      1,067  -629.641  -487.8429      32    1039.686   1198.809
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1039.686
. 
. *IRR=.5550174 (seq_ln)
. 
. *Model 10: Only democracies
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln ///
>   y199* y2* if durable2==2, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,922
                                                Wald chi2(30)     =     633.77
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -641.26882               Pseudo R2         =     0.3094

                                (Std. Err. adjusted for 106 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |    .070392   .0837992     0.84   0.401    -.0938513    .2346353
     polity2 |  -.3183746   .1301807    -2.45   0.014    -.5735241    -.063225
  public_cor |   .7458199   .6834481     1.09   0.275    -.5937138    2.085354
 physical_vd |  -6.333006   .8473121    -7.47   0.000    -7.993707   -4.672305
  express_vd |   4.491694   1.218227     3.69   0.000     2.104013    6.879376
  intensity2 |   .6324134   .1406616     4.50   0.000     .3567218     .908105
        info |   .0245758   .0061734     3.98   0.000     .0124762    .0366754
      pop_ln |   .5387155   .0919963     5.86   0.000      .358406     .719025
       y1992 |   .2340755   .6381145     0.37   0.714    -1.016606    1.484757
       y1993 |   .5073212   .5735624     0.88   0.376    -.6168405    1.631483
       y1994 |  -.4423353   .5639483    -0.78   0.433    -1.547654     .662983
       y1995 |  -.0757802    .520012    -0.15   0.884    -1.094985    .9434246
       y1996 |  -.3126792   .6181175    -0.51   0.613    -1.524167     .898809
       y1997 |   .2444298   .6243103     0.39   0.695    -.9791959    1.468055
       y1998 |  -.2909231   .5037098    -0.58   0.564    -1.278176      .69633
       y1999 |  -1.054779   .6518895    -1.62   0.106    -2.332459    .2229008
       y2000 |  -.2737314   .5115339    -0.54   0.593    -1.276319    .7288566
       y2001 |   .4200757    .487213     0.86   0.389    -.5348442    1.374996
       y2002 |  -.3627685   .4048119    -0.90   0.370    -1.156185    .4306483
       y2003 |  -.0146581   .4694882    -0.03   0.975    -.9348381    .9055218
       y2004 |   .2822483    .479215     0.59   0.556    -.6569957    1.221492
       y2005 |  -.3904316   .5096014    -0.77   0.444    -1.389232    .6083689
       y2006 |  -.5174238   .5352542    -0.97   0.334    -1.566503    .5316551
       y2007 |   .0349011   .4213307     0.08   0.934    -.7908918     .860694
       y2008 |   -.116404   .5253029    -0.22   0.825    -1.145979    .9131707
       y2009 |   .3729219   .6101944     0.61   0.541    -.8230372    1.568881
       y2010 |  -.0797922   .5349658    -0.15   0.881    -1.128306    .9687214
       y2011 |  -.2150627   .4323757    -0.50   0.619    -1.062503    .6323781
       y2012 |  -.1557821   .3544188    -0.44   0.660    -.8504302     .538866
       y2013 |  -.4884536   .4838884    -1.01   0.313    -1.436857    .4599502
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.695052   2.162455    -4.48   0.000    -13.93339   -5.456717
-------------+----------------------------------------------------------------
    /lnalpha |  -.2601378   .2719364                     -.7931234    .2728478
-------------+----------------------------------------------------------------
       alpha |   .7709453   .2096481                      .4524295      1.3137
------------------------------------------------------------------------------

. 
. *Identify observation in anocracies sample (will need for appendix analysis) 
. gen byte demo=e(sample)

. 
. *Store estimates
. est sto t2m4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        t2m4 |      1,922 -928.5335  -641.2688      32    1346.538   1524.494
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1346.538
. 
. *IRR=1.072929 (seq_ln)
. *IRR=.7273303 (polity level)
. 
. ****Create TABLE 2 for LaTex (basic table; I make some changes by hand once g
> enerated)****
. esttab t2m1 t2m2 t2m3 t2m4 using table2.tex, replace se aic obslast r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)") ///
>  varwidth(2) scalar(N_g) drop(y1* y2* _cons) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(output written to table2.tex)

. 
. *Generate Fig. 3
. 
. *run model 7
. quietly nbreg confirmed seq_ln polity public_cor physical_vd express_vd inten
> sity2 info pop_ln ///
>   y1992 y1993 y1994 y1995 y1996 y1997 y1998 y1999 y2000 y2001 y2002 y2003 y20
> 04 ///
>   y2005 y2006 y2007 y2008 y2009 y2010 y2011 y2012 y2013, cluster(ccode) nolog
>  

. 
. *drop durt* variables if you have not cleared from fig. 2 
. drop durat*

. 
. *create figure
. prgen seq_ln, from (0) to (5.5) generate(durat) rest(mean) ci 

nbreg: Predicted values as seq_ln varies from 0 to 5.5.

         seq_ln      polity2   public_cor  physical_vd   express_vd   intensity
> 2         info       pop_ln        y1992        y1993        y1994
x=    2.6653299    3.3714445    .51793065    .67177495    .66739466    .2353597
> 3    55.978912    16.128232    .04238706    .04322365    .04322365

          y1995        y1996        y1997        y1998        y1999        y200
> 0        y2001        y2002        y2003        y2004        y2005
x=    .04322365    .04322365    .04322365    .04350251    .04350251    .0435025
> 1    .04322365    .04350251    .04294479    .04322365    .04350251

          y2006        y2007        y2008        y2009        y2010        y201
> 1        y2012        y2013
x=    .04378137    .04378137    .04378137    .04378137    .04406023    .0437813
> 7    .04378137    .04378137

. label variable duratp1 "Probability of Journalist Killing" 

. label variable duratx "Regime-type Duration"  

. label variable duratp1lb "95% lower limit" 

. label variable duratp1ub "95% upper limit"

. 
. twoway (connected duratp1 duratx, lpattern(solid) lwidth(thin) msymbol(none) 
> ///
> yaxis(1)) (connected duratp1lb duratx, lpattern(dash) lwidth(thin) msymbol(no
> ne) ///
> yaxis(1)) (connected  duratp1ub duratx, lpattern(dash) lwidth(thin) msymbol(n
> one) yaxis(1) ///
> ylab(,nogrid))

. 
. *Note: I further stylize figure for manuscript
. 
. *Table 4: Survival analysis
. 
. *Create three datasets for different event thresholds
. *I green these out, but keep it in the replication file to demonstrate
. * how I created these data
. 
. *Killed count: one
. 
. *gen one=1 if confirmed!=0 & confirmed!=.
. *replace one=0 if one==.
. *tab one 
. 
. *Set data as survival
. 
. *stset year, failure(one) id(ccode) exit(year==2016)
. 
. *Save dataset
. 
. *export delimited "survival_one",replace
. 
. *Reload data
. 
. *clear 
. *use Solis_fpa_may2019
. *set more off
. 
. *Killed count: two
. 
. *gen two=.
. *replace two =1 if conf>=2
. *replace two =0 if conf<=1
. *tab two
. 
. *log durability
. 
. *gen seq_ln=ln(seq1)
. 
. *Set data as survival
. 
. *stset year, failure(two) id(ccode) exit(year==2016)
. 
. *Save dataset
. 
. *export delimited "survival_two",replace
. 
. *Reload data
. 
. *clear 
. *use Solis_fpa_may2019
. *set more off
. 
. *Killed count: three 
. 
. *gen three=.
. *replace three =1 if conf>=3
. *replace three =0 if conf<=2
. *tab three
. 
. *log durability
. 
. *gen seq_ln=ln(seq1)
. 
. *Set data as survival
. 
. *stset year, failure(three) id(ccode) exit(year==2016)
. 
. *Save dataset
. 
. *export delimited "survival_three",replace
. 
. ********************************************************************
. * SEE R SCRIPT FOR DURATION ANALYSIS LABELED `Solis_fpa_may2019.R' *
. ********************************************************************
. 
. ************************************
. ***********APPENDIX*****************
. ************************************
. 
. ***********************************
. ***********************************
. ** SECTION A: Summary Statistics **
. ***********************************
. ***********************************
. 
. *Create variable for unconfirmed journalist killed +1: Zero Inflated NBREG do
> es
. * not run otherwise
. gen fix=unconfirmed+1
(334 missing values generated)

. corr fix unconfirmed
(obs=3,918)

             |      fix unconf~d
-------------+------------------
         fix |   1.0000
 unconfirmed |   1.0000   1.0000


. 
. *             |      fix unconf~d
. *-------------+------------------
. *         fix |   1.0000
. * unconfirmed |   1.0000   1.0000
. 
. *Note, variables full, auto, ano, and demo created above; indicate their
. * respective samples
. 
. *Main Analysis (A.1)
. sum confirmed seq_ln polity2 qog physint speech intensity2 info pop_ln ///
>  public_cor express_vd physical_vd 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   confirmed |      4,252    .2890405    1.620976          0         33
      seq_ln |      3,725    2.679351    1.175199          0   5.370638
     polity2 |      4,056    3.296105    6.533446        -10         10
         qog |      3,049    .5493536    .2134088   .0416667          1
     physint |      3,200    4.761875     2.27113          0          8
-------------+---------------------------------------------------------
      speech |      3,207    .9787964    .7096514          0          2
  intensity2 |      3,918    .2291986    .5553784          0          2
        info |      3,854     56.7342     22.2736       1.51      98.12
      pop_ln |      3,889    15.94863    1.676756   11.16709   21.03389
  public_cor |      3,849    .5136602    .3009999   .0053535   .9814045
-------------+---------------------------------------------------------
  express_vd |      3,849    .6648465    .2770418    .009141   .9908468
 physical_vd |      3,849    .6744316    .2759458    .021823   .9926823

.  
. *Robustness checks (A.1)
. sum fix gmf fhfp gdp_ln gdppc_ln gdppc_cng hom_count hom_rate 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         fix |      3,918    1.218479    1.069094          1         17
         gmf |      3,914    2.169392    .7965382          1          3
        fhfp |      3,745    49.26756    23.93742          0        100
      gdp_ln |      3,774    23.85119    2.268242   18.09537   30.48709
    gdppc_ln |      3,771    7.905164    1.640184   4.174563   11.68877
-------------+---------------------------------------------------------
   gdppc_cng |      3,747    2.371246    7.019859  -62.22509   172.7522
   hom_count |      2,045    2536.498    6719.679          1      57091
    hom_rate |      2,104    8.192548    12.95538          0   139.1321

.  
. *Full (A.2)
. sum confirmed seq_ln seq1 polity2  intensity2 info pop_ln ///
>  public_cor express_vd physical_vd if full==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   confirmed |      3,586    .2615728    1.384779          0         33
      seq_ln |      3,586     2.66533    1.174397          0   5.370638
        seq1 |      3,586    26.94813    34.18803          1        215
     polity2 |      3,586    3.371445    6.418141        -10         10
  intensity2 |      3,586    .2353597    .5577791          0          2
-------------+---------------------------------------------------------
        info |      3,586    55.97891    22.34789       1.51      98.09
      pop_ln |      3,586    16.12823    1.530309   12.70671   21.03389
  public_cor |      3,586    .5179306    .3019919   .0053535   .9814045
  express_vd |      3,586    .6673947    .2760902    .021132   .9908468
 physical_vd |      3,586    .6717749    .2735365   .0225859   .9926823

.  
. *Autocracies (A.2)
. sum confirmed seq_ln seq1 polity2 intensity2 info pop_ln ///
>  public_cor express_vd physical_vd if auto==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   confirmed |        597    .2998325    2.184958          0         31
      seq_ln |        597    3.029731    1.169502          0   5.370638
        seq1 |        597    35.67839    40.01481          1        215
     polity2 |        597    -7.40871    1.301957        -10         -6
  intensity2 |        597    .2763819    .6149657          0          2
-------------+---------------------------------------------------------
        info |        597    48.59771    20.08867       4.03      92.51
      pop_ln |        597    16.13884    1.720693   13.02838   21.03389
  public_cor |        597    .6869151    .2101191   .0708087   .9688597
  express_vd |        597    .2508392    .1835381    .021132   .8133591
 physical_vd |        597    .4028926    .2549085   .0225859    .860082

. 
. *Anocracies (A.2)
. sum confirmed seq_ln seq1 polity2  intensity2 info pop_ln ///
>  public_cor express_vd physical_vd if ano==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   confirmed |      1,067    .3158388    1.314004          0         24
      seq_ln |      1,067     2.05769    .9001455          0   3.912023
        seq1 |      1,067    10.97657    8.525671          1         50
     polity2 |      1,067    .2024367    3.397973         -5          5
  intensity2 |      1,067    .3786317    .6519079          0          2
-------------+---------------------------------------------------------
        info |      1,067    42.95138    18.47659       1.51      92.34
      pop_ln |      1,067    16.08883    1.360997   12.93026     19.142
  public_cor |      1,067    .6961023    .2280804   .0077544   .9814045
  express_vd |      1,067    .5588061    .1937291   .0641406   .9302388
 physical_vd |      1,067    .5209738    .2324124   .0442503   .9614548

. 
. *Democracies (A.2)
. sum confirmed seq_ln seq1 polity2 intensity2 info pop_ln ///
>  public_cor express_vd physical_vd if demo==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   confirmed |      1,922     .219563     1.06561          0         33
      seq_ln |      1,922    2.889474    1.184613          0   5.327876
        seq1 |      1,922    33.10302    37.95899          1        206
     polity2 |      1,922    8.479188    1.464426          6         10
  intensity2 |      1,922    .1430801    .4549759          0          2
-------------+---------------------------------------------------------
        info |      1,922    65.50387    20.38195       8.54      98.09
      pop_ln |      1,922    16.14681    1.556491   12.70671    20.9809
  public_cor |      1,922    .3665295    .2796266   .0053535   .9631327
  express_vd |      1,922    .8570656    .1204461   .3248414   .9908468
 physical_vd |      1,922    .8390109    .1625369   .0916229   .9926823

. 
. *Correlation Matrix (A.3)
. pwcorr qog  public_cor 

             |      qog public~r
-------------+------------------
         qog |   1.0000 
  public_cor |  -0.7881   1.0000 

. pwcorr physint physical_vd 

             |  physint physic~d
-------------+------------------
     physint |   1.0000 
 physical_vd |   0.6885   1.0000 

. pwcorr speech express_vd  

             |   speech expres~d
-------------+------------------
      speech |   1.0000 
  express_vd |   0.6710   1.0000 

. 
. *************************************
. *************************************
. ** SECTION B: DESCRIPTIVE ANALYSIS **
. *************************************
. *************************************
. 
. *Data and code for descriptive analysis available upon request.
. 
. *Figure out countries with no polity score in sample (B.2 intro)
. ****Countries that have missing polity
. list country year if polity==.  & confirmed>=1 & inrange(year,1992,2014)

      +--------------------+
      |     country   year |
      |--------------------|
  10. | Afghanistan   2001 |
  15. | Afghanistan   2006 |
  16. | Afghanistan   2007 |
  17. | Afghanistan   2008 |
  18. | Afghanistan   2009 |
      |--------------------|
  19. | Afghanistan   2010 |
  20. | Afghanistan   2011 |
1797. |        Iraq   2003 |
1798. |        Iraq   2004 |
1799. |        Iraq   2005 |
      |--------------------|
1800. |        Iraq   2006 |
1801. |        Iraq   2007 |
1802. |        Iraq   2008 |
1803. |        Iraq   2009 |
2170. |     Lebanon   1992 |
      |--------------------|
2171. |     Lebanon   1993 |
2177. |     Lebanon   1999 |
3468. |     Somalia   2011 |
      +--------------------+

.         
. *      +--------------------+
. *      |     country   year |
. *      |--------------------|
. *  10. | Afghanistan   2001 |
. *  15. | Afghanistan   2006 |
. *  16. | Afghanistan   2007 |
. *  17. | Afghanistan   2008 |
. *  18. | Afghanistan   2009 |
. *      |--------------------|
. *  19. | Afghanistan   2010 |
. *  20. | Afghanistan   2011 |
. *1657. |        Iraq   2003 |
. *1658. |        Iraq   2004 |
. *1659. |        Iraq   2005 |
. *      |--------------------|
. *1660. |        Iraq   2006 |
. *1661. |        Iraq   2007 |
. *1662. |        Iraq   2008 |
. *1663. |        Iraq   2009 |
. *1998. |     Lebanon   1992 |
. *      |--------------------|
. *1999. |     Lebanon   1993 |
. *2005. |     Lebanon   1999 |
. *3198. |     Somalia   2011 |
. *      +--------------------+
.                         
. 
. *Number we lose from these
. tab confirmed if polity==.  & confirmed>=1. & inrange(year,1992,2014)

      (sum) |
  confirmed |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          2       11.11       11.11
          2 |          7       38.89       50.00
          3 |          1        5.56       55.56
          4 |          1        5.56       61.11
          9 |          1        5.56       66.67
         11 |          1        5.56       72.22
         14 |          1        5.56       77.78
         23 |          1        5.56       83.33
         24 |          1        5.56       88.89
         32 |          2       11.11      100.00
------------+-----------------------------------
      Total |         18      100.00

. 
. *  confirmed  Freq.       confirmed*Freq.
.       *1            2           2
.       *2            7           14
.       *3            1           3
.       *4            1           4
.       *9            1           9
.       *11           1           11
.       *14           1           14
.       *23           1           23
.       *24           1           24
.       *32           2           64
.                           *------------*
.                             *total=168
.                                                 
. ***************************************************
. ***************************************************
. ** SECTION C: MODEL'S CONTROL VARIABLES DECISOIN **
. ***************************************************
. ***************************************************
. 
. *Generate Table 5*
. 
. *Model 1: Global (NBREG)
. nbreg confirmed seq_ln polity qog physint speech intensity2 info pop_ln ///
>   y199* y2*, cluster(ccode) nolog
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      2,491
                                                Wald chi2(27)     =     652.80
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -943.86262               Pseudo R2         =     0.2419

                                (Std. Err. adjusted for 132 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4147484   .0886796    -4.68   0.000    -.5885572   -.2409396
     polity2 |   .0484784   .0216734     2.24   0.025     .0059993    .0909575
         qog |  -1.834329   .7834535    -2.34   0.019     -3.36987   -.2987887
     physint |  -.3937754   .0646856    -6.09   0.000    -.5205569   -.2669939
      speech |   .3202279   .1378818     2.32   0.020     .0499845    .5904713
  intensity2 |    .876815   .1422519     6.16   0.000     .5980064    1.155624
        info |   .0294872   .0054437     5.42   0.000     .0188179    .0401566
      pop_ln |   .3734167   .0827939     4.51   0.000     .2111437    .5356897
       y1992 |   .0884891   .4060512     0.22   0.827    -.7073566    .8843348
       y1993 |   .8340258   .3866615     2.16   0.031     .0761832    1.591869
       y1994 |    .794954   .4398802     1.81   0.071    -.0671953    1.657103
       y1995 |   .4014312   .3763249     1.07   0.286    -.3361521    1.139014
       y1996 |   .1088092   .4287766     0.25   0.800    -.7315776     .949196
       y1997 |  -.1042736   .3892987    -0.27   0.789    -.8672851    .6587379
       y1998 |   .1382009   .3809713     0.36   0.717    -.6084891    .8848909
       y1999 |  -.2775612   .4658704    -0.60   0.551     -1.19065    .6355281
       y2000 |   -.484316   .3881937    -1.25   0.212    -1.245162    .2765296
       y2001 |   .2913317   .4203177     0.69   0.488    -.5324758    1.115139
       y2002 |  -.4113312   .4289074    -0.96   0.338    -1.251974    .4293118
       y2003 |  -.2323672   .3438186    -0.68   0.499    -.9062392    .4415048
       y2004 |   .4325644   .3175331     1.36   0.173    -.1897891    1.054918
       y2005 |  -.0623076   .3061763    -0.20   0.839    -.6624022    .5377869
       y2006 |  -.7569267   .3087687    -2.45   0.014    -1.362102   -.1517512
       y2007 |   -.392278   .3046626    -1.29   0.198    -.9894057    .2048498
       y2008 |  -.5855509   .3301148    -1.77   0.076    -1.232564    .0614623
       y2009 |   .1510743   .3298872     0.46   0.647    -.4954928    .7976414
       y2010 |   .1591018   .3479981     0.46   0.648    -.5229619    .8411656
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -7.341823   1.349772    -5.44   0.000    -9.987328   -4.696319
-------------+----------------------------------------------------------------
    /lnalpha |   .5258509   .2633299                      .0097337    1.041968
-------------+----------------------------------------------------------------
       alpha |   1.691898   .4455273                      1.009781    2.834791
------------------------------------------------------------------------------

. 
. *Store estimates and create variable to identify sample
. est sto rra_full

. gen byte full_asalb=e(sample)

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
    rra_full |      2,491 -1244.965  -943.8626      29    1945.725   2114.518
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1945.725
. 
. *Model 2: Auto (NBREG)
. nbreg confirmed seq_ln polity qog physint speech intensity2 info pop_ln ///
>   y199* y2* if durable2==0, cluster(ccode) nolog
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =        391
                                                Wald chi2(27)     =   19913.12
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -97.822288               Pseudo R2         =     0.2330

                                 (Std. Err. adjusted for 37 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   -.744256   .2095748    -3.55   0.000    -1.155015    -.333497
     polity2 |   -.246413    .206712    -1.19   0.233    -.6515612    .1587351
         qog |  -2.121677   1.913091    -1.11   0.267    -5.871267    1.627912
     physint |  -.0762391   .1400188    -0.54   0.586     -.350671    .1981928
      speech |    .273593   .4897804     0.56   0.576    -.6863588    1.233545
  intensity2 |   1.262736   .4741107     2.66   0.008     .3334963    2.191976
        info |   .0265727   .0117743     2.26   0.024     .0034955    .0496499
      pop_ln |   .5314638   .1675523     3.17   0.002     .2030673    .8598603
       y1992 |  -1.624848   1.448878    -1.12   0.262    -4.464596      1.2149
       y1993 |  -.7854949   .8256732    -0.95   0.341    -2.403785    .8327949
       y1994 |  -.4073426   .7095952    -0.57   0.566    -1.798124    .9834385
       y1995 |  -23.66665   .8756775   -27.03   0.000    -25.38295   -21.95036
       y1996 |  -1.730275   1.685639    -1.03   0.305    -5.034067    1.573516
       y1997 |   -1.64467   1.044283    -1.57   0.115    -3.691427    .4020867
       y1998 |  -2.336569   1.100767    -2.12   0.034    -4.494032   -.1791054
       y1999 |  -.3113357    .950433    -0.33   0.743     -2.17415    1.551479
       y2000 |  -2.335165   1.178322    -1.98   0.048    -4.644633   -.0256966
       y2001 |  -1.936302   1.461404    -1.32   0.185    -4.800601    .9279964
       y2002 |   -23.8778   .7239543   -32.98   0.000    -25.29673   -22.45888
       y2003 |  -1.460029   1.415262    -1.03   0.302     -4.23389    1.313833
       y2004 |  -1.207652   1.551406    -0.78   0.436    -4.248352    1.833048
       y2005 |  -.7542858   1.333529    -0.57   0.572    -3.367955    1.859384
       y2006 |  -1.468362   1.278208    -1.15   0.251    -3.973604    1.036881
       y2007 |   -2.27735   1.045888    -2.18   0.029    -4.327253   -.2274467
       y2008 |  -24.12325   .6633017   -36.37   0.000     -25.4233   -22.82321
       y2009 |  -.4727703   .7918411    -0.60   0.550     -2.02475     1.07921
       y2010 |  -1.479934   1.390504    -1.06   0.287    -4.205271    1.245403
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -10.60399    4.23895    -2.50   0.012    -18.91218   -2.295804
-------------+----------------------------------------------------------------
    /lnalpha |   1.306543   .4558006                      .4131905    2.199896
-------------+----------------------------------------------------------------
       alpha |   3.693384   1.683447                      1.511633    9.024074
------------------------------------------------------------------------------

. 
. *Store estimates and create variable to identify sample
. est sto rra_auto

. gen byte auto_asalb=e(sample)

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
    rra_auto |        391 -127.5317  -97.82229      29    253.6446   368.7371
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=253.645
. 
. *Model 3: Ano (NBREG)
. nbreg confirmed seq_ln polity qog physint speech intensity2 info pop_ln ///
>   y199* y2* if durable2==1, cluster(ccode) nolog
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =        643
                                                Wald chi2(27)     =     559.69
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -264.85123               Pseudo R2         =     0.2550

                                 (Std. Err. adjusted for 61 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   -.650175   .1421835    -4.57   0.000    -.9288495   -.3715005
     polity2 |   .0447771    .041105     1.09   0.276    -.0357872    .1253413
         qog |  -1.570148   1.395951    -1.12   0.261    -4.306163    1.165866
     physint |  -.1760906    .084292    -2.09   0.037    -.3412999   -.0108814
      speech |   .0691999    .219082     0.32   0.752    -.3601929    .4985926
  intensity2 |   .9810251   .1523901     6.44   0.000     .6823459    1.279704
        info |   .0283295   .0095302     2.97   0.003     .0096506    .0470084
      pop_ln |   .5165494   .1359507     3.80   0.000     .2500909    .7830079
       y1992 |  -.2800274   .6486199    -0.43   0.666    -1.551299    .9912443
       y1993 |   .8651963   .5786561     1.50   0.135    -.2689488    1.999341
       y1994 |   .2468642   .5618256     0.44   0.660    -.8542938    1.348022
       y1995 |   1.113302    .474546     2.35   0.019     .1832092    2.043395
       y1996 |     .49146   .5831506     0.84   0.399    -.6514942    1.634414
       y1997 |  -1.422516   .9723525    -1.46   0.143    -3.328292    .4832596
       y1998 |   .4034463   .6304888     0.64   0.522    -.8322891    1.639182
       y1999 |  -.0936614   .6428854    -0.15   0.884    -1.353694    1.166371
       y2000 |  -.3594212   .7197414    -0.50   0.618    -1.770088    1.051246
       y2001 |  -.2718369   .7906231    -0.34   0.731     -1.82143    1.277756
       y2002 |  -.5280328   .6711994    -0.79   0.431     -1.84356    .7874939
       y2003 |  -.4917389   .5206532    -0.94   0.345      -1.5122    .5287226
       y2004 |   .5302965   .7057617     0.75   0.452    -.8529711    1.913564
       y2005 |   .3843083   .5964417     0.64   0.519     -.784696    1.553313
       y2006 |   -.990576   .5641014    -1.76   0.079    -2.096194    .1150425
       y2007 |  -.6344869   .6447668    -0.98   0.325    -1.898207    .6292328
       y2008 |   -.371328    .550579    -0.67   0.500    -1.450443     .707787
       y2009 |  -.3747034   .4813247    -0.78   0.436    -1.318082    .5686757
       y2010 |   .1811571   .5835479     0.31   0.756    -.9625758     1.32489
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -10.04983   2.159524    -4.65   0.000    -14.28242   -5.817245
-------------+----------------------------------------------------------------
    /lnalpha |   .1748809   .3357239                     -.4831258    .8328876
-------------+----------------------------------------------------------------
       alpha |   1.191104   .3998821                      .6168522     2.29995
------------------------------------------------------------------------------

. 
. *Store estimates and create variable to identify sample
. est sto rra_ano

. gen byte ano_asalb=e(sample)

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     rra_ano |        643 -355.5259  -264.8512      29    587.7025   717.2207
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=587.702
. 
. *Model 4: Demo (NBREG)
. nbreg confirmed seq_ln polity qog physint speech intensity2 info pop_ln ///
>   y199* y2* if durable2==2, cluster(ccode) nolog
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,457
                                                Wald chi2(27)     =     786.13
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -531.05027               Pseudo R2         =     0.2972

                                 (Std. Err. adjusted for 89 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   -.117002   .0998045    -1.17   0.241    -.3126153    .0786113
     polity2 |  -.2007216   .0783823    -2.56   0.010     -.354348   -.0470952
         qog |  -1.443858   .6531647    -2.21   0.027    -2.724038   -.1636789
     physint |  -.5020977   .0595566    -8.43   0.000    -.6188265   -.3853689
      speech |   .4564685   .1843176     2.48   0.013     .0952127    .8177242
  intensity2 |   .4693329   .1454745     3.23   0.001     .1842081    .7544578
        info |   .0239747   .0079609     3.01   0.003     .0083717    .0395778
      pop_ln |    .343322   .0915069     3.75   0.000     .1639718    .5226721
       y1992 |    .587756   .5706708     1.03   0.303    -.5307381     1.70625
       y1993 |   .7309898   .4295646     1.70   0.089    -.1109414    1.572921
       y1994 |   .0302359   .5555191     0.05   0.957    -1.058561    1.119033
       y1995 |  -.0026677   .5907868    -0.00   0.996    -1.160589    1.155253
       y1996 |  -.2086796     .52279    -0.40   0.690    -1.233329      .81597
       y1997 |   .1759582   .4472702     0.39   0.694    -.7006753    1.052592
       y1998 |  -.1142914   .4258048    -0.27   0.788    -.9488534    .7202706
       y1999 |  -.9854164   .6354615    -1.55   0.121    -2.230898    .2600652
       y2000 |  -.3774486   .4391175    -0.86   0.390    -1.238103    .4832058
       y2001 |    .555537   .5255198     1.06   0.290    -.4744629    1.585537
       y2002 |  -.2596043   .4351276    -0.60   0.551    -1.112439    .5932302
       y2003 |    -.14426   .3939502    -0.37   0.714    -.9163881    .6278682
       y2004 |   .4638448   .2872081     1.62   0.106    -.0990727    1.026762
       y2005 |  -.3187605   .3676339    -0.87   0.386     -1.03931    .4017886
       y2006 |  -.5461561     .36786    -1.48   0.138    -1.267149    .1748363
       y2007 |  -.0624735   .3902676    -0.16   0.873     -.827384    .7024369
       y2008 |   -.351529   .3937748    -0.89   0.372    -1.123314    .4202555
       y2009 |   .4720145   .3893376     1.21   0.225    -.2910732    1.235102
       y2010 |   .2930709   .3835502     0.76   0.445    -.4586736    1.044815
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -5.090941   1.618958    -3.14   0.002     -8.26404   -1.917841
-------------+----------------------------------------------------------------
    /lnalpha |  -.0591028   .2046876                     -.4602831    .3420774
-------------+----------------------------------------------------------------
       alpha |   .9426098   .1929405                       .631105    1.407869
------------------------------------------------------------------------------

. 
. *Store estimates and create variable to identify sample
. est sto rra_demo

. gen byte demo_asalb=e(sample)

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
    rra_demo |      1,457 -755.5959  -531.0503      29    1120.101    1273.34
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1120.101
. 
. ****Create TABLE 5 (Apx) for LaTex (basic table; I make some changes by hand 
> once generated)****
. esttab rra_full rra_auto rra_ano rra_demo using apx_revres1.tex, replace se a
> ic obslast r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" qog ///
> "Quality of Govt." physint "Physical Integrity" speech "Freedom of Speech" //
> /
>  intensity2 "Armed Conflict" info "Information Flows" pop_ln "Population (ln)
> ") ///
>  varwidth(2) scalar(N_g) drop(y1* y2* _cons) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(output written to apx_revres1.tex)

. 
. *Generate Table 6*
. 
. *Model 1: NBREG
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln ///
>   y199* y2* if inrange(year,1992,2011), cluster(ccode) nolog 
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      3,114
                                                Wald chi2(27)     =     685.54
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -1183.8689               Pseudo R2         =     0.2261

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3678215   .0852879    -4.31   0.000    -.5349828   -.2006602
     polity2 |   .0164786   .0260012     0.63   0.526    -.0344828    .0674401
  public_cor |   .5836357    .537658     1.09   0.278    -.4701545    1.637426
 physical_vd |  -4.342254   .7372685    -5.89   0.000    -5.787273   -2.897234
  express_vd |   3.461375   .7104137     4.87   0.000      2.06899     4.85376
  intensity2 |   1.315157   .1211283    10.86   0.000     1.077749    1.552564
        info |   .0265745   .0053346     4.98   0.000     .0161189    .0370302
      pop_ln |   .3792021   .0637625     5.95   0.000     .2542299    .5041743
       y1992 |   .0595235   .3726942     0.16   0.873    -.6709437    .7899908
       y1993 |   .6371705   .3540353     1.80   0.072    -.0567259    1.331067
       y1994 |   .7572694   .3630233     2.09   0.037     .0457568    1.468782
       y1995 |   .2532617   .3231107     0.78   0.433    -.3800236     .886547
       y1996 |  -.3785578    .394596    -0.96   0.337    -1.151952    .3948363
       y1997 |  -.2206552   .3928527    -0.56   0.574    -.9906324     .549322
       y1998 |  -.1888096   .3330458    -0.57   0.571    -.8415675    .4639482
       y1999 |  -.2162252   .4659476    -0.46   0.643    -1.129466    .6970153
       y2000 |  -.2921784   .3847055    -0.76   0.448    -1.046187    .4618305
       y2001 |   .1573632   .4069521     0.39   0.699    -.6402482    .9549746
       y2002 |  -.6415714   .4413871    -1.45   0.146    -1.506674    .2235314
       y2003 |  -.0371067   .3579522    -0.10   0.917    -.7386802    .6644668
       y2004 |   .2558829   .3209645     0.80   0.425     -.373196    .8849618
       y2005 |  -.0672494   .3153238    -0.21   0.831    -.6852726    .5507738
       y2006 |  -.5738361   .3410733    -1.68   0.092    -1.242327    .0946553
       y2007 |   .0211698   .3322298     0.06   0.949    -.6299886    .6723283
       y2008 |  -.4074561   .3505336    -1.16   0.245    -1.094489    .2795771
       y2009 |   .1614109   .3131272     0.52   0.606    -.4523071    .7751289
       y2010 |   .1753003   .3601218     0.49   0.626    -.5305254     .881126
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.431767   1.108541    -8.51   0.000    -11.60447   -7.259067
-------------+----------------------------------------------------------------
    /lnalpha |   .6747876   .2159281                      .2515763    1.097999
-------------+----------------------------------------------------------------
       alpha |   1.963616   .4239998                      1.286051     2.99816
------------------------------------------------------------------------------

. 
. *Store estimates and create variable to identify sample  
. est sto rr_full

. gen byte full_solisb=e(sample)

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     rr_full |      3,114 -1529.758  -1183.869      29    2425.738   2601.004
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=2425.738 
. 
. *Model 2: NBREG
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln ///
>   y199* y2* if durable2==0 & inrange(year,1992,2011), cluster(ccode) nolog 
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =        546
                                                Wald chi2(27)     =   10742.21
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -153.68832               Pseudo R2         =     0.2412

                                 (Std. Err. adjusted for 52 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4910573   .1832302    -2.68   0.007    -.8501818   -.1319327
     polity2 |   .3046363   .2314882     1.32   0.188    -.1490722    .7583449
  public_cor |  -1.233265   1.120239    -1.10   0.271    -3.428892    .9623626
 physical_vd |  -1.869973   1.335842    -1.40   0.162    -4.488176    .7482289
  express_vd |   .7444416    1.34219     0.55   0.579    -1.886202    3.375085
  intensity2 |   1.603285   .3061663     5.24   0.000      1.00321     2.20336
        info |   .0295758   .0121016     2.44   0.015      .005857    .0532945
      pop_ln |   .2564437   .1376808     1.86   0.063    -.0134058    .5262932
       y1992 |  -.5689006   .9045995    -0.63   0.529    -2.341883    1.204082
       y1993 |  -.0887439   .8646567    -0.10   0.918     -1.78344    1.605952
       y1994 |   .4713905   .7900426     0.60   0.551    -1.077065    2.019846
       y1995 |  -1.463332   .8474915    -1.73   0.084    -3.124384    .1977213
       y1996 |  -1.701546   1.220341    -1.39   0.163     -4.09337    .6902779
       y1997 |  -.9707879   1.071695    -0.91   0.365    -3.071272    1.129696
       y1998 |  -1.651365    .932317    -1.77   0.077    -3.478673    .1759422
       y1999 |  -.3237905   1.092876    -0.30   0.767    -2.465788    1.818207
       y2000 |  -1.856061   1.285318    -1.44   0.149    -4.375238    .6631155
       y2001 |  -1.216281   1.514595    -0.80   0.422    -4.184833     1.75227
       y2002 |  -2.939094   .9735224    -3.02   0.003    -4.847162   -1.031025
       y2003 |  -1.083546   1.134715    -0.95   0.340    -3.307547    1.140455
       y2004 |  -1.251831   1.404528    -0.89   0.373    -4.004656    1.500993
       y2005 |  -.7348161   .9707715    -0.76   0.449    -2.637493    1.167861
       y2006 |  -.2382921    1.18842    -0.20   0.841    -2.567552    2.090968
       y2007 |   .0100802   1.303309     0.01   0.994    -2.544358    2.564518
       y2008 |  -19.27271   .7065367   -27.28   0.000     -20.6575   -17.88792
       y2009 |  -.2283911   .6977845    -0.33   0.743    -1.596024    1.139241
       y2010 |  -1.232977   1.477704    -0.83   0.404    -4.129224     1.66327
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -3.329652   2.590228    -1.29   0.199    -8.406406    1.747102
-------------+----------------------------------------------------------------
    /lnalpha |   1.015505   .6158637                     -.1915659    2.222575
-------------+----------------------------------------------------------------
       alpha |   2.760756    1.70025                      .8256652    9.231073
------------------------------------------------------------------------------

. 
. *Store estimates and create variable to identify sample
. est sto rr_auto

. gen byte auto_solisb=e(sample)

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     rr_auto |        546 -202.5405  -153.6883      29    365.3766   490.1526
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=365.3766
. 
. *Model 3: NBREG
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln ///
>   y199* y2* if durable2==1 & inrange(year,1992,2011), cluster(ccode) nolog 
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =        920
                                                Wald chi2(27)     =     389.87
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -397.87298               Pseudo R2         =     0.2325

                                 (Std. Err. adjusted for 84 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   -.654323   .1540421    -4.25   0.000      -.95624    -.352406
     polity2 |   .0343535   .0395266     0.87   0.385    -.0431172    .1118242
  public_cor |   .6772926    .785773     0.86   0.389    -.8627942    2.217379
 physical_vd |  -4.027837   1.168001    -3.45   0.001    -6.317077   -1.738598
  express_vd |   4.425838   .9223549     4.80   0.000     2.618055     6.23362
  intensity2 |   1.446778   .1665011     8.69   0.000     1.120441    1.773114
        info |   .0316211    .008564     3.69   0.000     .0148359    .0484062
      pop_ln |   .1917351   .1068838     1.79   0.073    -.0177534    .4012236
       y1992 |  -.1726787   .5601266    -0.31   0.758    -1.270507    .9251492
       y1993 |    .538652    .479501     1.12   0.261    -.4011527    1.478457
       y1994 |   .8061363   .5422155     1.49   0.137    -.2565866    1.868859
       y1995 |   .6550906   .5025626     1.30   0.192    -.3299139    1.640095
       y1996 |  -.2151281   .6136705    -0.35   0.726      -1.4179     .987644
       y1997 |  -1.543859   .6266781    -2.46   0.014    -2.772126   -.3155928
       y1998 |  -.2031945   .5575331    -0.36   0.716    -1.295939    .8895503
       y1999 |  -.1370856   .5485042    -0.25   0.803    -1.212134    .9379628
       y2000 |  -.1487681   .5716962    -0.26   0.795    -1.269272     .971736
       y2001 |  -.5014294   .7777247    -0.64   0.519    -2.025742    1.022883
       y2002 |  -1.239252    .722075    -1.72   0.086    -2.654493    .1759892
       y2003 |  -.2196981   .6205658    -0.35   0.723    -1.435985    .9965884
       y2004 |   .0281039   .7412586     0.04   0.970    -1.424736    1.480944
       y2005 |   .5047574   .5880636     0.86   0.391     -.647826    1.657341
       y2006 |  -.7046022   .5456117    -1.29   0.197    -1.773981     .364777
       y2007 |   .0322873   .5599452     0.06   0.954    -1.065185     1.12976
       y2008 |  -.2026379   .4716553    -0.43   0.667    -1.127065    .7217896
       y2009 |   .1212033   .4992102     0.24   0.808    -.8572308    1.099637
       y2010 |   .4868519   .5681268     0.86   0.391    -.6266562     1.60036
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -7.042287   1.542789    -4.56   0.000     -10.0661   -4.018475
-------------+----------------------------------------------------------------
    /lnalpha |   .4779832   .2714034                     -.0539576    1.009924
-------------+----------------------------------------------------------------
       alpha |   1.612818   .4377244                      .9474723    2.745392
------------------------------------------------------------------------------

. 
. *Store estimates and create variable to identify sample
. est sto rr_ano

. gen byte ano_solisb=e(sample)

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
      rr_ano |        920 -518.3947   -397.873      29     853.746   993.6528
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=853.746
. 
. *Model 4: NBREG
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln ///
>   y199* y2* if durable2==2 & inrange(year,1992,2011), cluster(ccode) nolog
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,648
                                                Wald chi2(27)     =     552.35
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -552.84848               Pseudo R2         =     0.3108

                                (Std. Err. adjusted for 104 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   .0446966   .0825243     0.54   0.588    -.1170482    .2064413
     polity2 |   -.249809   .1151062    -2.17   0.030    -.4754129    -.024205
  public_cor |   1.011445   .6378626     1.59   0.113    -.2387423    2.261633
 physical_vd |  -6.224154   .8701473    -7.15   0.000    -7.929611   -4.518697
  express_vd |    4.11415   1.139814     3.61   0.000     1.880155    6.348146
  intensity2 |   .6615299   .1267645     5.22   0.000      .413076    .9099837
        info |   .0251311   .0061016     4.12   0.000     .0131722      .03709
      pop_ln |   .5083861   .0736806     6.90   0.000     .3639748    .6527975
       y1992 |   .4195885   .5354591     0.78   0.433    -.6298919    1.469069
       y1993 |   .7013894   .4507625     1.56   0.120    -.1820889    1.584868
       y1994 |  -.2504285   .4700194    -0.53   0.594     -1.17165    .6707927
       y1995 |   .1512022   .4342418     0.35   0.728    -.6998961    1.002301
       y1996 |  -.1130484   .5115812    -0.22   0.825    -1.115729    .8896323
       y1997 |     .47829   .5117762     0.93   0.350    -.5247729    1.481353
       y1998 |  -.0529835   .3619849    -0.15   0.884    -.7624609    .6564939
       y1999 |  -.8336283   .5728674    -1.46   0.146    -1.956428    .2891712
       y2000 |  -.0737402   .3910289    -0.19   0.850    -.8401428    .6926623
       y2001 |   .6059938   .4575617     1.32   0.185    -.2908107    1.502798
       y2002 |  -.1512509   .4358417    -0.35   0.729    -1.005485    .7029831
       y2003 |   .2000885   .4148424     0.48   0.630    -.6129876    1.013165
       y2004 |   .4822118   .2946647     1.64   0.102    -.0953204    1.059744
       y2005 |  -.2030112   .3668388    -0.55   0.580    -.9220021    .5159797
       y2006 |  -.3254608   .3930359    -0.83   0.408    -1.095797    .4448755
       y2007 |   .2355498   .3716164     0.63   0.526     -.492805    .9639046
       y2008 |    .093252    .424707     0.22   0.826    -.7391584    .9256624
       y2009 |   .5736245   .4468849     1.28   0.199    -.3022538    1.449503
       y2010 |   .1281328   .4173835     0.31   0.759    -.6899239    .9461894
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.782798   1.807236    -5.41   0.000    -13.32491   -6.240681
-------------+----------------------------------------------------------------
    /lnalpha |  -.3151403   .2347589                     -.7752593    .1449788
-------------+----------------------------------------------------------------
       alpha |   .7296865   .1713004                      .4605843    1.156015
------------------------------------------------------------------------------

. 
. *Store estimates and create variable to identify sample
. est sto rr_demo

. gen byte demo_solisb=e(sample)

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     rr_demo |      1,648  -802.158  -552.8485      29    1163.697   1320.509
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1163.697
. 
. ****Create TABLE 6 (Apx) for LaTex (basic table; I make some changes by hand 
> once generated)****
. esttab rr_full rr_auto rr_ano rr_demo using rr2011_solis.tex, replace se aic 
> obslast r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" gmf "Global Media Freedom" publi
> c_cor ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" polity2 "Poli
> ty" ///
>  info "Information Flows" pop_ln "Population (ln)" fix "CPJ Unconfirmed" ///
>  gdp_ln "GDP (ln)" gdppc_ln "GDP p/c (ln)" gdppc_cng "$\Delta$ GDP p/c" ) ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(output written to rr2011_solis.tex)

. 
. *Generate data for Table 7*
. 
. *Find data for percentage less of observations
. *Instructions: take samples sizes from Appendix tables 5 and 6, then compare
. 
. *Global samples:
. * asal controls = 2,491 
. * vdem controls = 3,114
. display 3114 - 2491
623

. * = 623
. display (623/3114)*100
20.006423

. *~20%
. 
. *Auto samples:
. * asal controls = 391 
. * vdem controls = 546
. display 546 - 391
155

. * = 155
. display (155/546)*100
28.388278

. *~28%
. 
. *Ano samples:
. * asal controls = 643 
. * vdem controls = 920
. display 920 - 643
277

. * = 277
. display (277/920)*100
30.108696

. *~30%
. 
. *Demo samples:
. * asal controls = 1457 
. * vdem controls = 1648
. display 1648 - 1457
191

. * = 191
. display (191/1648)*100
11.589806

. *~11%
. 
. *Find data for percentage less of journalists killed in missing observations
. 
. *Rerun models for appendix table 5 and 6
. 
. *appendix table 5
. nbreg confirmed seq_ln polity qog physint speech intensity2 info pop_ln ///
>   y199* y2*, cluster(ccode) nolog
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      2,491
                                                Wald chi2(27)     =     652.80
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -943.86262               Pseudo R2         =     0.2419

                                (Std. Err. adjusted for 132 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4147484   .0886796    -4.68   0.000    -.5885572   -.2409396
     polity2 |   .0484784   .0216734     2.24   0.025     .0059993    .0909575
         qog |  -1.834329   .7834535    -2.34   0.019     -3.36987   -.2987887
     physint |  -.3937754   .0646856    -6.09   0.000    -.5205569   -.2669939
      speech |   .3202279   .1378818     2.32   0.020     .0499845    .5904713
  intensity2 |    .876815   .1422519     6.16   0.000     .5980064    1.155624
        info |   .0294872   .0054437     5.42   0.000     .0188179    .0401566
      pop_ln |   .3734167   .0827939     4.51   0.000     .2111437    .5356897
       y1992 |   .0884891   .4060512     0.22   0.827    -.7073566    .8843348
       y1993 |   .8340258   .3866615     2.16   0.031     .0761832    1.591869
       y1994 |    .794954   .4398802     1.81   0.071    -.0671953    1.657103
       y1995 |   .4014312   .3763249     1.07   0.286    -.3361521    1.139014
       y1996 |   .1088092   .4287766     0.25   0.800    -.7315776     .949196
       y1997 |  -.1042736   .3892987    -0.27   0.789    -.8672851    .6587379
       y1998 |   .1382009   .3809713     0.36   0.717    -.6084891    .8848909
       y1999 |  -.2775612   .4658704    -0.60   0.551     -1.19065    .6355281
       y2000 |   -.484316   .3881937    -1.25   0.212    -1.245162    .2765296
       y2001 |   .2913317   .4203177     0.69   0.488    -.5324758    1.115139
       y2002 |  -.4113312   .4289074    -0.96   0.338    -1.251974    .4293118
       y2003 |  -.2323672   .3438186    -0.68   0.499    -.9062392    .4415048
       y2004 |   .4325644   .3175331     1.36   0.173    -.1897891    1.054918
       y2005 |  -.0623076   .3061763    -0.20   0.839    -.6624022    .5377869
       y2006 |  -.7569267   .3087687    -2.45   0.014    -1.362102   -.1517512
       y2007 |   -.392278   .3046626    -1.29   0.198    -.9894057    .2048498
       y2008 |  -.5855509   .3301148    -1.77   0.076    -1.232564    .0614623
       y2009 |   .1510743   .3298872     0.46   0.647    -.4954928    .7976414
       y2010 |   .1591018   .3479981     0.46   0.648    -.5229619    .8411656
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -7.341823   1.349772    -5.44   0.000    -9.987328   -4.696319
-------------+----------------------------------------------------------------
    /lnalpha |   .5258509   .2633299                      .0097337    1.041968
-------------+----------------------------------------------------------------
       alpha |   1.691898   .4455273                      1.009781    2.834791
------------------------------------------------------------------------------

.   
. *Create variable to identify sample
. gen byte full_asal=e(sample)

. 
. *appendix table 6
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln ///
>   y199* y2* if inrange(year,1992,2011), cluster(ccode) nolog 
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      3,114
                                                Wald chi2(27)     =     685.54
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -1183.8689               Pseudo R2         =     0.2261

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3678215   .0852879    -4.31   0.000    -.5349828   -.2006602
     polity2 |   .0164786   .0260012     0.63   0.526    -.0344828    .0674401
  public_cor |   .5836357    .537658     1.09   0.278    -.4701545    1.637426
 physical_vd |  -4.342254   .7372685    -5.89   0.000    -5.787273   -2.897234
  express_vd |   3.461375   .7104137     4.87   0.000      2.06899     4.85376
  intensity2 |   1.315157   .1211283    10.86   0.000     1.077749    1.552564
        info |   .0265745   .0053346     4.98   0.000     .0161189    .0370302
      pop_ln |   .3792021   .0637625     5.95   0.000     .2542299    .5041743
       y1992 |   .0595235   .3726942     0.16   0.873    -.6709437    .7899908
       y1993 |   .6371705   .3540353     1.80   0.072    -.0567259    1.331067
       y1994 |   .7572694   .3630233     2.09   0.037     .0457568    1.468782
       y1995 |   .2532617   .3231107     0.78   0.433    -.3800236     .886547
       y1996 |  -.3785578    .394596    -0.96   0.337    -1.151952    .3948363
       y1997 |  -.2206552   .3928527    -0.56   0.574    -.9906324     .549322
       y1998 |  -.1888096   .3330458    -0.57   0.571    -.8415675    .4639482
       y1999 |  -.2162252   .4659476    -0.46   0.643    -1.129466    .6970153
       y2000 |  -.2921784   .3847055    -0.76   0.448    -1.046187    .4618305
       y2001 |   .1573632   .4069521     0.39   0.699    -.6402482    .9549746
       y2002 |  -.6415714   .4413871    -1.45   0.146    -1.506674    .2235314
       y2003 |  -.0371067   .3579522    -0.10   0.917    -.7386802    .6644668
       y2004 |   .2558829   .3209645     0.80   0.425     -.373196    .8849618
       y2005 |  -.0672494   .3153238    -0.21   0.831    -.6852726    .5507738
       y2006 |  -.5738361   .3410733    -1.68   0.092    -1.242327    .0946553
       y2007 |   .0211698   .3322298     0.06   0.949    -.6299886    .6723283
       y2008 |  -.4074561   .3505336    -1.16   0.245    -1.094489    .2795771
       y2009 |   .1614109   .3131272     0.52   0.606    -.4523071    .7751289
       y2010 |   .1753003   .3601218     0.49   0.626    -.5305254     .881126
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.431767   1.108541    -8.51   0.000    -11.60447   -7.259067
-------------+----------------------------------------------------------------
    /lnalpha |   .6747876   .2159281                      .2515763    1.097999
-------------+----------------------------------------------------------------
       alpha |   1.963616   .4239998                      1.286051     2.99816
------------------------------------------------------------------------------

. 
. *Create variable to identify sample
. gen byte full_solis=e(sample)

. 
. *Instructions: tabulate journalists killed, multiply `confirmed' columns and 
> `Freq.'
. * column. Then add these results.
. 
. *Number of journalists killed in table 5 samples 
. tab confirmed if full_asal==1 & inrange(year,1992,2011)

      (sum) |
  confirmed |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,219       89.08       89.08
          1 |        157        6.30       95.38
          2 |         57        2.29       97.67
          3 |         26        1.04       98.72
          4 |          9        0.36       99.08
          5 |          9        0.36       99.44
          6 |          2        0.08       99.52
          7 |          3        0.12       99.64
          8 |          5        0.20       99.84
          9 |          1        0.04       99.88
         18 |          1        0.04       99.92
         24 |          1        0.04       99.96
         33 |          1        0.04      100.00
------------+-----------------------------------
      Total |      2,491      100.00

. *587
. tab confirmed if full_asal==1 & inrange(year,1992,2011) & durable2==0 

      (sum) |
  confirmed |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        366       93.61       93.61
          1 |         18        4.60       98.21
          2 |          4        1.02       99.23
          6 |          1        0.26       99.49
          8 |          1        0.26       99.74
         18 |          1        0.26      100.00
------------+-----------------------------------
      Total |        391      100.00

. *58
. tab confirmed if full_asal==1 & inrange(year,1992,2011) & durable2==1 

      (sum) |
  confirmed |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        565       87.87       87.87
          1 |         44        6.84       94.71
          2 |         14        2.18       96.89
          3 |          9        1.40       98.29
          4 |          1        0.16       98.44
          5 |          5        0.78       99.22
          6 |          1        0.16       99.38
          7 |          1        0.16       99.53
          8 |          2        0.31       99.84
         24 |          1        0.16      100.00
------------+-----------------------------------
      Total |        643      100.00

. *181
. tab confirmed if full_asal==1 & inrange(year,1992,2011) & durable2==2 

      (sum) |
  confirmed |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,288       88.40       88.40
          1 |         95        6.52       94.92
          2 |         39        2.68       97.60
          3 |         17        1.17       98.76
          4 |          8        0.55       99.31
          5 |          4        0.27       99.59
          7 |          2        0.14       99.73
          8 |          2        0.14       99.86
          9 |          1        0.07       99.93
         33 |          1        0.07      100.00
------------+-----------------------------------
      Total |      1,457      100.00

. *348
. 
. *Number of journalists killed in table 6 samples 
. tab confirmed if full_solis==1 & inrange(year,1992,2011)

      (sum) |
  confirmed |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,784       89.40       89.40
          1 |        193        6.20       95.60
          2 |         64        2.06       97.66
          3 |         31        1.00       98.65
          4 |          9        0.29       98.94
          5 |         12        0.39       99.33
          6 |          3        0.10       99.42
          7 |          4        0.13       99.55
          8 |          6        0.19       99.74
          9 |          3        0.10       99.84
         10 |          1        0.03       99.87
         15 |          1        0.03       99.90
         18 |          1        0.03       99.94
         24 |          1        0.03       99.97
         33 |          1        0.03      100.00
------------+-----------------------------------
      Total |      3,114      100.00

. *731
. tab confirmed if full_solis==1 & inrange(year,1992,2011) & durable2==0 

      (sum) |
  confirmed |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        506       92.67       92.67
          1 |         28        5.13       97.80
          2 |          6        1.10       98.90
          5 |          1        0.18       99.08
          6 |          1        0.18       99.27
          8 |          1        0.18       99.45
          9 |          1        0.18       99.63
         15 |          1        0.18       99.82
         18 |          1        0.18      100.00
------------+-----------------------------------
      Total |        546      100.00

. *101
. tab confirmed if full_solis==1 & inrange(year,1992,2011) & durable2==1 

      (sum) |
  confirmed |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        807       87.72       87.72
          1 |         64        6.96       94.67
          2 |         18        1.96       96.63
          3 |         13        1.41       98.04
          4 |          1        0.11       98.15
          5 |          7        0.76       98.91
          6 |          2        0.22       99.13
          7 |          2        0.22       99.35
          8 |          3        0.33       99.67
          9 |          1        0.11       99.78
         10 |          1        0.11       99.89
         24 |          1        0.11      100.00
------------+-----------------------------------
      Total |        920      100.00

. *271
. tab confirmed if full_solis==1 & inrange(year,1992,2011) & durable2==2 

      (sum) |
  confirmed |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,471       89.26       89.26
          1 |        101        6.13       95.39
          2 |         40        2.43       97.82
          3 |         18        1.09       98.91
          4 |          8        0.49       99.39
          5 |          4        0.24       99.64
          7 |          2        0.12       99.76
          8 |          2        0.12       99.88
          9 |          1        0.06       99.94
         33 |          1        0.06      100.00
------------+-----------------------------------
      Total |      1,648      100.00

. *359
. 
. *Generate Table 8*
. 
. *Generate variable for missingness
. gen missing=.
(4,252 missing values generated)

. replace missing =1 if full_solis==1 & full_asal!=1
(683 real changes made)

. replace missing=0 if missing==.
(3,569 real changes made)

. 
. *Model likelihood of missingness w/polity
. logit missing polity   ///
>   y199* y2* if full_solis==1 & inrange(year,1992,2011),  cluster(ccode) nolog
>  
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Logistic regression                             Number of obs     =      3,114
                                                Wald chi2(20)     =      39.91
                                                Prob > chi2       =     0.0051
Log pseudolikelihood = -1542.3734               Pseudo R2         =     0.0585

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
     missing |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     polity2 |  -.0822681   .0239394    -3.44   0.001    -.1291885   -.0353477
       y1992 |   .4718758   .1841916     2.56   0.010      .110867    .8328846
       y1993 |    .466207   .1731994     2.69   0.007     .1267424    .8056717
       y1994 |   .5260936   .1725216     3.05   0.002     .1879574    .8642297
       y1995 |    .450686   .1669112     2.70   0.007      .123546     .777826
       y1996 |    .403124   .1636864     2.46   0.014     .0823045    .7239434
       y1997 |   .4357187   .1659947     2.62   0.009      .110375    .7610623
       y1998 |    .410194    .159799     2.57   0.010     .0969936    .7233944
       y1999 |   .0558698   .1156592     0.48   0.629    -.1708181    .2825577
       y2000 |   .0779458   .1135778     0.69   0.493    -.1446626    .3005542
       y2001 |  -.1162955   .0740802    -1.57   0.116      -.26149    .0288991
       y2002 |   .0186714   .0881579     0.21   0.832     -.154115    .1914578
       y2003 |  -.0966802   .0728223    -1.33   0.184    -.2394092    .0460489
       y2004 |  -.0043791   .0766813    -0.06   0.954    -.1546717    .1459135
       y2005 |  -.0361106   .0581441    -0.62   0.535    -.1500709    .0778498
       y2006 |   .0499622    .061061     0.82   0.413    -.0697151    .1696394
       y2007 |   .0058575   .0408537     0.14   0.886    -.0742143    .0859293
       y2008 |   .0139339   .0394348     0.35   0.724     -.063357    .0912247
       y2009 |   .0585607    .058079     1.01   0.313    -.0552721    .1723935
       y2010 |   .0121791   .0362835     0.34   0.737    -.0589352    .0832934
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -1.258715   .2253493    -5.59   0.000    -1.700392   -.8170387
------------------------------------------------------------------------------

. 
. *Store estimates
. est sto missing1

. 
. *Predict probability of missingness
. predict pr_a if e(sample)
(option pr assumed; Pr(missing))
(1,138 missing values generated)

. *predict idxhat if e(sample), xb
. *generate phat2 = exp(idxhat)/(1+exp(idxhat))
. 
. *Model likelihood of missingness w/polity and other controls
. logit missing polity pop_ln gdppc_ln intensity2  ///
>   y199* y2* if full_solis==1 & inrange(year,1992,2011), cluster(ccode) nolog 
note: y2011 omitted because of collinearity
note: y2012 omitted because of collinearity
note: y2013 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Logistic regression                             Number of obs     =      3,037
                                                Wald chi2(23)     =      91.44
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -948.02183               Pseudo R2         =     0.3907

                                (Std. Err. adjusted for 158 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
     missing |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     polity2 |  -.0856402   .0344391    -2.49   0.013    -.1531396   -.0181407
      pop_ln |  -1.281618   .2326975    -5.51   0.000    -1.737697   -.8255396
    gdppc_ln |  -.8824678   .1820385    -4.85   0.000    -1.239257   -.5256789
  intensity2 |   .4360846   .2606885     1.67   0.094    -.0748555    .9470246
       y1992 |  -1.081295   .3776731    -2.86   0.004    -1.821521   -.3410693
       y1993 |  -1.040406   .3599162    -2.89   0.004    -1.745829   -.3349831
       y1994 |  -.9429221   .3598387    -2.62   0.009    -1.648193   -.2376511
       y1995 |  -.6024693   .3363814    -1.79   0.073    -1.261765    .0568262
       y1996 |  -.6195003   .3226913    -1.92   0.055    -1.251964     .012963
       y1997 |  -.5574348   .3299288    -1.69   0.091    -1.204083    .0892138
       y1998 |  -.6346621   .3323197    -1.91   0.056    -1.285997    .0166725
       y1999 |  -1.206384   .2850122    -4.23   0.000    -1.764998   -.6477707
       y2000 |  -1.156417   .2866919    -4.03   0.000    -1.718323   -.5945114
       y2001 |  -1.401817   .2599676    -5.39   0.000    -1.911345   -.8922902
       y2002 |  -1.145433    .258289    -4.43   0.000     -1.65167    -.639196
       y2003 |  -1.134682     .21177    -5.36   0.000    -1.549744   -.7196209
       y2004 |  -.8784972   .2008363    -4.37   0.000    -1.272129   -.4848653
       y2005 |  -.7721682   .1579112    -4.89   0.000    -1.081668    -.462668
       y2006 |  -.5171812   .1374493    -3.76   0.000    -.7865769   -.2477856
       y2007 |  -.3987863   .0880817    -4.53   0.000    -.5714233   -.2261493
       y2008 |  -.2347242   .0591691    -3.97   0.000    -.3506935   -.1187548
       y2009 |   -.178952   .0931783    -1.92   0.055    -.3615781    .0036741
       y2010 |  -.1400929   .0358076    -3.91   0.000    -.2102745   -.0699114
       y2011 |          0  (omitted)
       y2012 |          0  (omitted)
       y2013 |          0  (omitted)
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |   25.77818    4.55331     5.66   0.000     16.85386    34.70251
------------------------------------------------------------------------------

. est sto missing2

. 
. *Predict probability of missingness
. predict pr_b if e(sample)
(option pr assumed; Pr(missing))
(1,215 missing values generated)

. *predict idxhat if e(sample), xb
. *generate phat2 = exp(idxhat)/(1+exp(idxhat))
. 
. ****Create TABLE 8 (Apx) for LaTex (basic table; I make some changes by hand 
> once generated)****
. esttab missing1 missing2 using missingness.tex, replace se aic obslast r2 ///
> coeflabel( polity2 "Polity" pop_ln "Population (ln)" gdppc_ln "GDP p/c (ln)" 
> ///
>  conflict "Conflict") ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(output written to missingness.tex)

. 
. *Generate data Table 9*
. 
. *Get predicted probabilities for models in table 8 by regime type
. 
. *Model 1, table 8
. *Avg by regime type
. * -> Get the mean from these commands
. sum pr_a if durable2==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        pr_a |        546    .3907238    .0588894   .2928874   .5224997

. sum pr_a if durable2==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        pr_a |        920     .253777    .0670664   .1435207   .4203636

. sum pr_a if durable2==2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        pr_a |      1,648    .1433192    .0315118   .0999579   .2268419

. 
. *Model 2, table 8
. *Avg by regime type
. * -> Get the mean from these commands
. sum pr_b if durable2==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        pr_b |        515    .3262997    .3070361   .0002795   .9728145

. sum pr_b if durable2==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        pr_b |        888    .3122502    .2680956   .0009663   .9780771

. sum pr_b if durable2==2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        pr_b |      1,634    .1154697    .1921803   .0000292   .8617786

. 
. *******************************************************************
. *******************************************************************
. ** SECTION D: COX REGRESSION MODEL MULTIPLE EVENT SPECIFICATIONS **
. *******************************************************************
. *******************************************************************
. 
. * SEE R SCRIPT FOR SURVIVAL ANALYSIS LABELED `Solis_fpa_may2019.R' 
. 
. ***********************************************
. ***********************************************
. ** SECTION E: ALTERNATE MODEL SPECIFICATIONS **
. ***********************************************
. ***********************************************
. 
. *Table 11: Logit (after running this post-FPA acceptance, some of the results
>  
. *          differ from the appendix document after the tenths and hundredths 
. *          place in some analyses. However, my inferences remain the same.)  
. 
. *Model 1: Global
. logit logit seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2* , cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Logistic regression                             Number of obs     =      3,586
                                                Wald chi2(30)     =     454.67
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -832.47843               Pseudo R2         =     0.3180

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
       logit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3244966   .0844312    -3.84   0.000    -.4899786   -.1590145
     polity2 |    .047816   .0302282     1.58   0.114    -.0114301    .1070622
  public_cor |   1.424787   .5689303     2.50   0.012     .3097041     2.53987
 physical_vd |  -4.671495   .9561296    -4.89   0.000    -6.545475   -2.797516
  express_vd |   3.823038   .9107468     4.20   0.000     2.038007    5.608069
  intensity2 |     1.2938   .1365746     9.47   0.000     1.026119    1.561481
        info |   .0340153   .0068482     4.97   0.000      .020593    .0474376
      pop_ln |   .4875023   .0761932     6.40   0.000     .3381663    .6368383
       y1992 |   .2007226   .4775171     0.42   0.674    -.7351938    1.136639
       y1993 |   .6209061   .4669685     1.33   0.184    -.2943354    1.536148
       y1994 |   .5855857   .4212451     1.39   0.164    -.2400394    1.411211
       y1995 |   .3347811    .482159     0.69   0.487    -.6102332    1.279795
       y1996 |   -.049467   .4915011    -0.10   0.920    -1.012791    .9138574
       y1997 |  -.1252958   .4282711    -0.29   0.770    -.9646916    .7141001
       y1998 |   .0830511   .4583504     0.18   0.856    -.8152991    .9814013
       y1999 |  -.8300171   .5381144    -1.54   0.123    -1.884702    .2246677
       y2000 |  -.0590308   .4159389    -0.14   0.887    -.8742559    .7561944
       y2001 |   .3448373   .4155236     0.83   0.407    -.4695739    1.159249
       y2002 |  -.6772384   .3932689    -1.72   0.085    -1.448031    .0935545
       y2003 |   .0603078   .3995141     0.15   0.880    -.7227255    .8433411
       y2004 |   .2056264     .36934     0.56   0.578    -.5182668    .9295195
       y2005 |   .0107132   .3953183     0.03   0.978    -.7640964    .7855228
       y2006 |  -.3842114   .4442007    -0.86   0.387    -1.254829     .486406
       y2007 |   .1075314   .3796991     0.28   0.777    -.6366651    .8517279
       y2008 |   -.537561   .4159629    -1.29   0.196    -1.352833    .2777112
       y2009 |   .2187013   .4176309     0.52   0.601    -.5998402    1.037243
       y2010 |    .160851   .4121033     0.39   0.696    -.6468566    .9685586
       y2011 |   .0726556   .3771897     0.19   0.847    -.6666227    .8119339
       y2012 |   .1129482   .3757786     0.30   0.764    -.6235643    .8494608
       y2013 |   -.573398   .3733549    -1.54   0.125     -1.30516    .1583642
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -12.59014   1.561561    -8.06   0.000    -15.65074   -9.529532
------------------------------------------------------------------------------

. 
. *store estimates
. est sto log1

. 
. *AIC  
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        log1 |      3,586 -1220.578  -832.4784      31    1726.957   1918.685
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=2860.384
. 
. *Model 2: Auto
. logit logit seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2* if durable2==0, cluster(ccode) nolog
note: y2008 != 0 predicts failure perfectly
      y2008 dropped and 22 obs not used

note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Logistic regression                             Number of obs     =        575
                                                Wald chi2(29)     =    1065.23
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -111.59412               Pseudo R2         =     0.2817

                                 (Std. Err. adjusted for 52 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
       logit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.5186938   .1975596    -2.63   0.009    -.9059036   -.1314841
     polity2 |   .1025259   .2939013     0.35   0.727    -.4735101    .6785619
  public_cor |   .2161744   1.399526     0.15   0.877    -2.526846    2.959195
 physical_vd |  -1.810595   1.551666    -1.17   0.243    -4.851804    1.230614
  express_vd |   1.588205   2.383915     0.67   0.505    -3.084183    6.260593
  intensity2 |    1.58362   .3477879     4.55   0.000     .9019686    2.265272
        info |    .032273   .0122378     2.64   0.008     .0082874    .0562586
      pop_ln |   .2306291   .1289741     1.79   0.074    -.0221554    .4834137
       y1992 |   .3689972   1.027868     0.36   0.720    -1.645586    2.383581
       y1993 |   .0764057   .9346175     0.08   0.935    -1.755411    1.908222
       y1994 |   1.159763   .9054362     1.28   0.200    -.6148596    2.934385
       y1995 |  -.0400054   1.190811    -0.03   0.973    -2.373953    2.293942
       y1996 |  -.3607233   1.189674    -0.30   0.762    -2.692441    1.970994
       y1997 |  -.5014589   1.273735    -0.39   0.694    -2.997935    1.995017
       y1998 |   .1424263   1.108523     0.13   0.898    -2.030239    2.315092
       y1999 |  -.0972891   1.171604    -0.08   0.934     -2.39359    2.199012
       y2000 |  -.5522271   1.348804    -0.41   0.682    -3.195834     2.09138
       y2001 |   -.139012   1.587161    -0.09   0.930    -3.249791    2.971767
       y2002 |  -1.086002   1.012774    -1.07   0.284    -3.071003    .8989993
       y2003 |   .2852174   1.245453     0.23   0.819    -2.155826    2.726261
       y2004 |   .1353031   1.149127     0.12   0.906    -2.116945    2.387551
       y2005 |   .7722133    1.06282     0.73   0.467    -1.310876    2.855302
       y2006 |   .7951059   1.281015     0.62   0.535    -1.715637    3.305849
       y2007 |   .5979974   1.089577     0.55   0.583    -1.537535     2.73353
       y2008 |          0  (omitted)
       y2009 |   1.374618   1.097134     1.25   0.210    -.7757241    3.524961
       y2010 |  -.1069951   1.483981    -0.07   0.943    -3.015545    2.801555
       y2011 |   .9886012   .9315881     1.06   0.289     -.837278     2.81448
       y2012 |   .9041011   .7969144     1.13   0.257    -.6578225    2.466025
       y2013 |   .1483534   .1833258     0.81   0.418    -.2109584    .5076653
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -6.866074   3.667897    -1.87   0.061    -14.05502    .3228722
------------------------------------------------------------------------------

. 
. *store estimates
. est sto log2

. 
. *AIC  
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        log2 |        575 -155.3598  -111.5941      30    283.1882   413.8193
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=419.092
. 
. *Model 3: Ano
. logit logit seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2* if durable2==1, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Logistic regression                             Number of obs     =      1,067
                                                Wald chi2(30)     =     233.52
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -297.96199               Pseudo R2         =     0.2782

                                 (Std. Err. adjusted for 86 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
       logit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4942641   .1398344    -3.53   0.000    -.7683344   -.2201938
     polity2 |   .0697732   .0430344     1.62   0.105    -.0145727     .154119
  public_cor |   .4637252   .8515512     0.54   0.586    -1.205284    2.132735
 physical_vd |  -4.070007   1.235478    -3.29   0.001      -6.4915   -1.648514
  express_vd |   3.958022   1.179019     3.36   0.001     1.647187    6.268858
  intensity2 |   1.209614   .1810785     6.68   0.000     .8547066    1.564521
        info |   .0346586   .0096584     3.59   0.000     .0157284    .0535888
      pop_ln |   .3013159   .1298496     2.32   0.020     .0468154    .5558165
       y1992 |  -.3141349   .7442159    -0.42   0.673    -1.772771    1.144501
       y1993 |  -.0394757   .6285062    -0.06   0.950    -1.271325    1.192374
       y1994 |    .591799   .6502169     0.91   0.363    -.6826028    1.866201
       y1995 |   .5044155   .6833854     0.74   0.460    -.8349953    1.843826
       y1996 |   -.774526   .8880743    -0.87   0.383     -2.51512    .9660677
       y1997 |  -1.308412   .8047329    -1.63   0.104     -2.88566    .2688352
       y1998 |  -.2488496   .7817043    -0.32   0.750    -1.780962    1.283263
       y1999 |  -1.011009   .7916624    -1.28   0.202    -2.562639    .5406207
       y2000 |  -.4573045   .7151749    -0.64   0.523    -1.859022    .9444126
       y2001 |  -.9186108   .8813922    -1.04   0.297    -2.646108    .8088862
       y2002 |   -1.50943   .9111126    -1.66   0.098    -3.295177    .2763183
       y2003 |  -.2348369   .6859867    -0.34   0.732    -1.579346    1.109672
       y2004 |  -.2740133   .7534083    -0.36   0.716    -1.750667     1.20264
       y2005 |  -.0300425   .6972886    -0.04   0.966    -1.396703    1.336618
       y2006 |  -.7074521   .7013466    -1.01   0.313    -2.082066     .667162
       y2007 |   -.228944   .6743116    -0.34   0.734     -1.55057    1.092682
       y2008 |  -.6947638   .7293907    -0.95   0.341    -2.124343    .7348157
       y2009 |  -.2625134   .7201164    -0.36   0.715    -1.673916    1.148889
       y2010 |   .1316221     .69456     0.19   0.850     -1.22969    1.492935
       y2011 |  -.1567212    .608675    -0.26   0.797    -1.349702     1.03626
       y2012 |  -.2401128   .7017145    -0.34   0.732    -1.615448    1.135222
       y2013 |  -.3937695   .6105246    -0.64   0.519    -1.590376    .8028368
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -8.489618     2.2626    -3.75   0.000    -12.92423   -4.055003
------------------------------------------------------------------------------

. 
. *store estimates
. est sto log3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        log3 |      1,067 -412.8255   -297.962      31     657.924   812.0748
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1039.686
. 
. *Model 4: Demo
. logit logit seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2* if durable2==2, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Logistic regression                             Number of obs     =      1,922
                                                Wald chi2(30)     =     605.58
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -367.92576               Pseudo R2         =     0.4286

                                (Std. Err. adjusted for 106 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
       logit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   .0978156   .1262122     0.78   0.438    -.1495557    .3451869
     polity2 |  -.3371432   .1560711    -2.16   0.031    -.6430369   -.0312495
  public_cor |   1.851806   .8513867     2.18   0.030     .1831185    3.520493
 physical_vd |  -7.086634    1.37116    -5.17   0.000    -9.774058   -4.399211
  express_vd |   5.440507   1.515274     3.59   0.000     2.470624    8.410391
  intensity2 |   1.017242   .2072567     4.91   0.000     .6110267    1.423458
        info |   .0398543   .0106529     3.74   0.000     .0189749    .0607336
      pop_ln |   .6390069   .1167599     5.47   0.000     .4101618    .8678521
       y1992 |   .5868947   .8440403     0.70   0.487    -1.067394    2.241183
       y1993 |    1.60202   .7730285     2.07   0.038     .0869119    3.117128
       y1994 |    .309827   .6477563     0.48   0.632    -.9597521    1.579406
       y1995 |   .3212738   .7224119     0.44   0.657    -1.094628    1.737175
       y1996 |   .7783652   .7462397     1.04   0.297    -.6842376    2.240968
       y1997 |   .7454797   .6527604     1.14   0.253    -.5339073    2.024867
       y1998 |   .1450997   .7583447     0.19   0.848    -1.341229    1.631428
       y1999 |  -1.173602   .9010185    -1.30   0.193    -2.939566    .5923619
       y2000 |   .4627473   .6508263     0.71   0.477    -.8128487    1.738343
       y2001 |    1.39797   .5780334     2.42   0.016     .2650451    2.530894
       y2002 |  -.1170932   .5697599    -0.21   0.837    -1.233802    .9996156
       y2003 |   .1713985   .6084855     0.28   0.778    -1.021211    1.364008
       y2004 |   .6296126   .5638034     1.12   0.264    -.4754217    1.734647
       y2005 |  -.1267552   .6148635    -0.21   0.837    -1.331865    1.078355
       y2006 |  -.3264298     .64671    -0.50   0.614    -1.593958    .9410984
       y2007 |   .4124271   .5168819     0.80   0.425    -.6006428    1.425497
       y2008 |  -.0268725   .5517439    -0.05   0.961    -1.108271    1.054526
       y2009 |   .4531385   .5745481     0.79   0.430     -.672955    1.579232
       y2010 |   .2921745   .5690079     0.51   0.608    -.8230605     1.40741
       y2011 |   .1623841   .5597557     0.29   0.772    -.9347168    1.259485
       y2012 |   .2602906   .4924546     0.53   0.597    -.7049028    1.225484
       y2013 |  -.9466271   .6045385    -1.57   0.117    -2.131501    .2382466
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |   -13.5528   2.613554    -5.19   0.000    -18.67527   -8.430325
------------------------------------------------------------------------------

. 
. *store estimates
. est sto log4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        log4 |      1,922 -643.9242  -367.9258      31    797.8515   970.2463
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1346.538
. 
. ****Create TABLE 11 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab log1 log2 log3 log4 using apx_logit.tex, replace se aic obslast r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" unconfirmed "CPJ Unconfirm
> ed") ///
>  varwidth(2) scalar(N_g) drop(y1* y2* _cons) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(output written to apx_logit.tex)

. 
. *Table 12: Rare Events Logit
. 
. *Model 1: Global
. relogit logit seq_ln polity public_cor physical_vd express_vd intensity2 info
>  pop_ln ///
>   y1992 y1993 y1994 y1995 y1996 y1997 y1998 y1999 y2000 y2001 y2002 y2003 y20
> 04 ///
>   y2005 y2006 y2007 y2008 y2009 y2010 y2011 y2012 y2013, cluster(ccode)
(666 missing values generated)


Corrected logit estimates                             Number of obs =     3586

------------------------------------------------------------------------------
             |               Robust
       logit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3190345   .0837075    -3.81   0.000    -.4830983   -.1549708
     polity2 |   .0463508   .0299691     1.55   0.122    -.0123876    .1050891
  public_cor |   1.392224    .564054     2.47   0.014      .286699     2.49775
 physical_vd |   -4.58524   .9479346    -4.84   0.000    -6.443157   -2.727322
  express_vd |    3.74557   .9029408     4.15   0.000     1.975839    5.515301
  intensity2 |    1.26498    .135404     9.34   0.000     .9995928    1.530367
        info |   .0333063   .0067895     4.91   0.000      .019999    .0466135
      pop_ln |   .4796927   .0755402     6.35   0.000     .3316366    .6277487
       y1992 |   .1994865   .4734244     0.42   0.673    -.7284082    1.127381
       y1993 |   .6075403   .4629662     1.31   0.189    -.2998566    1.514937
       y1994 |   .5724331   .4176346     1.37   0.170    -.2461156    1.390982
       y1995 |   .3293255   .4780265     0.69   0.491    -.6075892     1.26624
       y1996 |  -.0439926   .4872884    -0.09   0.928    -.9990604    .9110751
       y1997 |  -.1185061   .4246004    -0.28   0.780    -.9507075    .7136953
       y1998 |   .0810667   .4544218     0.18   0.858    -.8095838    .9717171
       y1999 |   -.799095   .5335021    -1.50   0.134     -1.84474      .24655
       y2000 |  -.0556663   .4123739    -0.13   0.893    -.8639042    .7525716
       y2001 |   .3360092   .4119621     0.82   0.415    -.4714217     1.14344
       y2002 |  -.6521757   .3898982    -1.67   0.094    -1.416362    .1120107
       y2003 |    .059956   .3960899     0.15   0.880    -.7163659     .836278
       y2004 |   .2012239   .3661744     0.55   0.583    -.5164648    .9189127
       y2005 |   .0103379     .39193     0.03   0.979    -.7578308    .7785067
       y2006 |  -.3737484   .4403935    -0.85   0.396    -1.236904     .489407
       y2007 |   .1050149   .3764447     0.28   0.780    -.6328031     .842833
       y2008 |  -.5213943   .4123976    -1.26   0.206    -1.329679    .2868902
       y2009 |   .2125454   .4140514     0.51   0.608    -.5989804    1.024071
       y2010 |   .1563653   .4085712     0.38   0.702    -.6444195    .9571501
       y2011 |   .0696229   .3739569     0.19   0.852    -.6633191    .8025649
       y2012 |   .1090653   .3725578     0.29   0.770    -.6211346    .8392653
       y2013 |   -.554955   .3701549    -1.50   0.134    -1.280445    .1705352
       _cons |   -12.3568   1.548177    -7.98   0.000    -15.39117   -9.322425
------------------------------------------------------------------------------

. 
. *store estimates
. est sto rel1

. 
. *Model 2: Auto
. relogit logit seq_ln polity public_cor physical_vd express_vd intensity2 info
>  pop_ln ///
>   y1992 y1993 y1994 y1995 y1996 y1997 y1998 y1999 y2000 y2001 y2002 y2003 y20
> 04 ///
>   y2005 y2006 y2007  y2009 y2010 y2011 y2012 y2013 if durable2==0, cluster(cc
> ode)
(666 missing values generated)


Corrected logit estimates                             Number of obs =      597

------------------------------------------------------------------------------
             |               Robust
       logit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   -.438474   .1898364    -2.31   0.021    -.8105464   -.0664015
     polity2 |   .0681455   .2835912     0.24   0.810     -.487683     .623974
  public_cor |   .1924764   1.345862     0.14   0.886    -2.445365    2.830318
 physical_vd |  -1.578426   1.517984    -1.04   0.298    -4.553619    1.396768
  express_vd |   1.467479    2.31968     0.63   0.527     -3.07901    6.013967
  intensity2 |   1.380552   .3444797     4.01   0.000     .7053845     2.05572
        info |   .0285295   .0119534     2.39   0.017     .0051013    .0519576
      pop_ln |   .1980951   .1241816     1.60   0.111    -.0452963    .4414866
       y1992 |   1.128559    1.07924     1.05   0.296    -.9867116     3.24383
       y1993 |   .9233123   .9676344     0.95   0.340    -.9732162    2.819841
       y1994 |   1.804929   .9458332     1.91   0.056    -.0488695    3.658729
       y1995 |   .8266823    1.23236     0.67   0.502    -1.588698    3.242063
       y1996 |    .543202   1.219694     0.45   0.656    -1.847355    2.933759
       y1997 |   .4203485   1.267131     0.33   0.740    -2.063182    2.903879
       y1998 |   .9365897   1.130743     0.83   0.408    -1.279626    3.152805
       y1999 |   .7754906   1.216759     0.64   0.524    -1.609312    3.160293
       y2000 |   .5456451   1.368555     0.40   0.690    -2.136673    3.227963
       y2001 |   .8939245   1.597321     0.56   0.576    -2.236768    4.024617
       y2002 |   .0700189   1.068481     0.07   0.948    -2.024165    2.164203
       y2003 |   1.094848   1.227126     0.89   0.372    -1.310274    3.499971
       y2004 |   .9626536   1.136621     0.85   0.397    -1.265082    3.190389
       y2005 |   1.469875   1.078255     1.36   0.173    -.6434656    3.583216
       y2006 |   1.523381   1.278463     1.19   0.233    -.9823602    4.029123
       y2007 |   1.361813    1.11591     1.22   0.222    -.8253308    3.548956
       y2009 |   1.980708   1.105975     1.79   0.073    -.1869627     4.14838
       y2010 |   .9028202   1.418935     0.64   0.525    -1.878241    3.683882
       y2011 |   1.689451   .8984403     1.88   0.060    -.0714601    3.450361
       y2012 |   1.609303   .9070582     1.77   0.076    -.1684987    3.387104
       y2013 |   1.110724   .3081857     3.60   0.000     .5066914    1.714757
       _cons |    -6.9561   3.471731    -2.00   0.045    -13.76057   -.1516329
------------------------------------------------------------------------------

. 
. *store estimates
. est sto rel2

. 
. *Model 3: Ano
. relogit logit seq_ln polity public_cor physical_vd express_vd intensity2 info
>  pop_ln ///
>   y1992 y1993 y1994 y1995 y1996 y1997 y1998 y1999 y2000 y2001 y2002 y2003 y20
> 04 ///
>   y2005 y2006 y2007 y2008 y2009 y2010 y2011 y2012 y2013 if durable2==1, clust
> er(ccode)
(666 missing values generated)


Corrected logit estimates                             Number of obs =     1067

------------------------------------------------------------------------------
             |               Robust
       logit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4691891   .1358864    -3.45   0.001    -.7355215   -.2028567
     polity2 |   .0662247   .0418194     1.58   0.113    -.0157398    .1481892
  public_cor |    .407181   .8275092     0.49   0.623    -1.214707    2.029069
 physical_vd |  -3.857132   1.200597    -3.21   0.001    -6.210259   -1.504005
  express_vd |   3.727961   1.145732     3.25   0.001     1.482368    5.973555
  intensity2 |   1.139797   .1759661     6.48   0.000     .7949095    1.484684
        info |   .0326762   .0093858     3.48   0.000     .0142804    .0510719
      pop_ln |   .2865639   .1261835     2.27   0.023     .0392487    .5338791
       y1992 |  -.2831411   .7232043    -0.39   0.695    -1.700595    1.134313
       y1993 |  -.0266686   .6107615    -0.04   0.965    -1.223739    1.170402
       y1994 |   .5565134   .6318592     0.88   0.378    -.6819079    1.794935
       y1995 |    .477121   .6640913     0.72   0.472    -.8244739    1.778716
       y1996 |  -.6919305   .8630012    -0.80   0.423    -2.383382    .9995207
       y1997 |  -1.110417   .7820127    -1.42   0.156    -2.643134    .4222993
       y1998 |  -.2310611   .7596343    -0.30   0.761    -1.719917    1.257795
       y1999 |  -.9180896   .7693113    -1.19   0.233    -2.425912    .5897327
       y2000 |  -.4149183   .6949833    -0.60   0.550     -1.77706    .9472239
       y2001 |  -.8097507   .8565077    -0.95   0.344    -2.488475    .8689736
       y2002 |  -1.299257   .8853888    -1.47   0.142    -3.034587    .4360735
       y2003 |   -.203684   .6666191    -0.31   0.760    -1.510234    1.102865
       y2004 |  -.2348398   .7321372    -0.32   0.748    -1.669802    1.200123
       y2005 |  -.0091328   .6776019    -0.01   0.989    -1.337208    1.318943
       y2006 |  -.6320637   .6815454    -0.93   0.354    -1.967868    .7037407
       y2007 |  -.1995082   .6552736    -0.30   0.761    -1.483821    1.084804
       y2008 |  -.6316012   .7087977    -0.89   0.373    -2.020819    .7576167
       y2009 |    -.24029   .6997852    -0.34   0.731    -1.611844    1.131264
       y2010 |   .1284367   .6749503     0.19   0.849    -1.194442    1.451315
       y2011 |  -.1428811   .5914902    -0.24   0.809    -1.302181    1.016418
       y2012 |  -.2222821   .6819029    -0.33   0.744    -1.558787    1.114223
       y2013 |  -.3614166   .5932876    -0.61   0.542    -1.524239    .8014057
       _cons |    -8.0158    2.19872    -3.65   0.000    -12.32521   -3.706389
------------------------------------------------------------------------------

. 
. *store estimates  
. est sto rel3

. 
. *Model 4: Demo
. relogit logit seq_ln polity public_cor physical_vd express_vd intensity2 info
>  pop_ln ///
>   y1992 y1993 y1994 y1995 y1996 y1997 y1998 y1999 y2000 y2001 y2002 y2003 y20
> 04 ///
>   y2005 y2006 y2007 y2008 y2009 y2010 y2011 y2012 y2013 if durable2==2, clust
> er(ccode)
(666 missing values generated)


Corrected logit estimates                             Number of obs =     1922

------------------------------------------------------------------------------
             |               Robust
       logit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   .0943561   .1242088     0.76   0.447    -.1490887    .3378009
     polity2 |  -.3323294   .1535938    -2.16   0.030    -.6333677   -.0312911
  public_cor |   1.749301   .8378726     2.09   0.037     .1071013    3.391502
 physical_vd |  -6.748028   1.349395    -5.00   0.000    -9.392794   -4.103262
  express_vd |     5.1311   1.491222     3.44   0.001     2.208357    8.053842
  intensity2 |   .9572979   .2039669     4.69   0.000     .5575301    1.357066
        info |    .038064   .0104838     3.63   0.000      .017516    .0586119
      pop_ln |   .6162699   .1149065     5.36   0.000     .3910572    .8414826
       y1992 |   .5804275   .8306429     0.70   0.485    -1.047603    2.208458
       y1993 |   1.529483   .7607582     2.01   0.044     .0384241    3.020542
       y1994 |   .3202323   .6374745     0.50   0.615    -.9291947    1.569659
       y1995 |   .3232445    .710945     0.45   0.649    -1.070182    1.716671
       y1996 |   .7512852   .7343946     1.02   0.306    -.6881017    2.190672
       y1997 |   .7082579   .6423991     1.10   0.270    -.5508213    1.967337
       y1998 |   .1396796   .7463075     0.19   0.852    -1.323056    1.602415
       y1999 |  -1.081546   .8867166    -1.22   0.223    -2.819479    .6563865
       y2000 |   .4369305   .6404957     0.68   0.495     -.818418    1.692279
       y2001 |   1.323904   .5688583     2.33   0.020     .2089622    2.438846
       y2002 |  -.1087389   .5607161    -0.19   0.846    -1.207722    .9902444
       y2003 |    .158315    .598827     0.26   0.791    -1.015364    1.331994
       y2004 |   .5937628   .5548541     1.07   0.285    -.4937312    1.681257
       y2005 |  -.1220051   .6051037    -0.20   0.840    -1.307987    1.063976
       y2006 |  -.3126358   .6364447    -0.49   0.623    -1.560045     .934773
       y2007 |   .3857459   .5086774     0.76   0.448    -.6112435    1.382735
       y2008 |  -.0282671   .5429861    -0.05   0.958      -1.0925    1.035966
       y2009 |   .4259801   .5654283     0.75   0.451    -.6822389    1.534199
       y2010 |   .2723246   .5599761     0.49   0.627    -.8252083    1.369858
       y2011 |   .1484964   .5508706     0.27   0.787    -.9311902    1.228183
       y2012 |   .2402797   .4846379     0.50   0.620    -.7095932    1.190153
       y2013 |  -.8820727   .5949427    -1.48   0.138    -2.048139    .2839935
       _cons |  -12.91827   2.572069    -5.02   0.000    -17.95943   -7.877104
------------------------------------------------------------------------------

. 
. *store estimates
. est sto rel4

. 
. ****Create TABLE 12 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab rel1 rel2 rel3 rel4 using apx_rel.tex, replace se aic obslast r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" unconfirmed "CPJ Unconfirm
> ed") ///
>  varwidth(2) scalar(N_g) drop(y1* y2* _cons) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(output written to apx_rel.tex)

.   
. *Table 13: Ordinal 1
. 
. *Model 1: Global
. ologit ord1 seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2*, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Ordered logistic regression                     Number of obs     =      3,586
                                                Wald chi2(30)     =     703.71
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1067.4798               Pseudo R2         =     0.2802

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
        ord1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   -.345925   .0877373    -3.94   0.000    -.5178869   -.1739632
     polity2 |   .0458791   .0300201     1.53   0.126    -.0129592    .1047175
  public_cor |   1.433413    .617079     2.32   0.020     .2239601    2.642865
 physical_vd |  -4.924713   .9623926    -5.12   0.000    -6.810968   -3.038458
  express_vd |   4.039143   .8893228     4.54   0.000     2.296102    5.782184
  intensity2 |   1.320294   .1473294     8.96   0.000     1.031534    1.609054
        info |   .0362792   .0065444     5.54   0.000     .0234525    .0491059
      pop_ln |     .48085   .0780851     6.16   0.000      .327806     .633894
       y1992 |   .2915935   .4700545     0.62   0.535    -.6296963    1.212883
       y1993 |   .7669917   .4708094     1.63   0.103    -.1557778    1.689761
       y1994 |   .6164027   .4111664     1.50   0.134    -.1894686    1.422274
       y1995 |   .2938796   .4539444     0.65   0.517     -.595835    1.183594
       y1996 |   -.124573   .4789203    -0.26   0.795     -1.06324    .8140936
       y1997 |  -.1181793   .4135015    -0.29   0.775    -.9286274    .6922687
       y1998 |   .0914314   .4574723     0.20   0.842    -.8051978    .9880605
       y1999 |  -.6874231   .5427068    -1.27   0.205    -1.751109    .3762626
       y2000 |  -.0457197   .3985545    -0.11   0.909    -.8268721    .7354327
       y2001 |    .306699   .3996985     0.77   0.443    -.4766957    1.090094
       y2002 |   -.600448   .3815294    -1.57   0.116    -1.348232     .147336
       y2003 |   .0788233    .386583     0.20   0.838    -.6788654     .836512
       y2004 |   .2094444   .3456031     0.61   0.544    -.4679253    .8868141
       y2005 |   .1216554    .393991     0.31   0.757    -.6505528    .8938637
       y2006 |  -.3828513   .4220196    -0.91   0.364    -1.209995    .4442919
       y2007 |   .0228606   .3476495     0.07   0.948    -.6585198     .704241
       y2008 |  -.2895794   .3979664    -0.73   0.467    -1.069579    .4904203
       y2009 |   .1821716   .3733112     0.49   0.626    -.5495048    .9138481
       y2010 |   .2130049   .4056159     0.53   0.599    -.5819877    1.007998
       y2011 |   .0766396   .3382726     0.23   0.821    -.5863624    .7396416
       y2012 |    .051901   .3116532     0.17   0.868    -.5589281    .6627301
       y2013 |  -.3412606   .3731901    -0.91   0.360      -1.0727    .3901785
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
-------------+----------------------------------------------------------------
       /cut1 |   12.56923   1.580748                      9.471017    15.66744
       /cut2 |   13.84567   1.594002                      10.72148    16.96985
------------------------------------------------------------------------------

.  
. *store estimates
. est sto ordv1_1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     ordv1_1 |      3,586 -1482.937   -1067.48      32     2198.96   2396.873
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=2198.96
. 
. *Model 2: Auto
. ologit ord1 seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2* if durable2==0, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Ordered logistic regression                     Number of obs     =        597
                                                Wald chi2(30)     =   11338.66
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -137.74043               Pseudo R2         =     0.2567

                                 (Std. Err. adjusted for 52 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
        ord1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4786334   .2156301    -2.22   0.026    -.9012606   -.0560061
     polity2 |   .0859951   .3029633     0.28   0.777     -.507802    .6797922
  public_cor |   .1824719   1.496398     0.12   0.903    -2.750414    3.115358
 physical_vd |  -1.897743   1.747063    -1.09   0.277    -5.321924    1.526438
  express_vd |   1.865501   2.374338     0.79   0.432    -2.788116    6.519118
  intensity2 |   1.751782   .4159359     4.21   0.000      .936563    2.567002
        info |   .0363756   .0139448     2.61   0.009     .0090443    .0637069
      pop_ln |   .2049737   .1435209     1.43   0.153    -.0763221    .4862695
       y1992 |   .2421812   1.123027     0.22   0.829    -1.958911    2.443273
       y1993 |   .1743068   1.119139     0.16   0.876    -2.019165    2.367779
       y1994 |   1.132204   .9921569     1.14   0.254    -.8123881    3.076796
       y1995 |  -.3864312   1.273893    -0.30   0.762    -2.883216    2.110353
       y1996 |  -.6473144   1.261486    -0.51   0.608    -3.119782    1.825154
       y1997 |  -.6066007    1.40752    -0.43   0.666    -3.365289    2.152088
       y1998 |  -.3111196   1.192039    -0.26   0.794    -2.647472    2.025233
       y1999 |   -.132121   1.282047    -0.10   0.918    -2.644886    2.380644
       y2000 |  -.8175131   1.406535    -0.58   0.561    -3.574271    1.939245
       y2001 |  -.3883004   1.661867    -0.23   0.815    -3.645501      2.8689
       y2002 |  -1.684487   1.090967    -1.54   0.123    -3.822743    .4537683
       y2003 |   .2655318    1.28103     0.21   0.836     -2.24524    2.776304
       y2004 |  -.3216648   1.228053    -0.26   0.793    -2.728604    2.085274
       y2005 |   .1873388   1.158667     0.16   0.872    -2.083606    2.458284
       y2006 |   .5238543   1.323771     0.40   0.692    -2.070689    3.118398
       y2007 |   .3295808   1.138552     0.29   0.772     -1.90194    2.561101
       y2008 |  -14.84337   .8644036   -17.17   0.000    -16.53757   -13.14917
       y2009 |   .8823454   1.179701     0.75   0.454    -1.429826    3.194517
       y2010 |  -.4105444   1.555724    -0.26   0.792    -3.459707    2.638618
       y2011 |   .8610057   .9540813     0.90   0.367    -1.008959    2.730971
       y2012 |    .780619   .7423985     1.05   0.293    -.6744553    2.235693
       y2013 |    .198543   .2353372     0.84   0.399    -.2627095    .6597955
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
-------------+----------------------------------------------------------------
       /cut1 |   6.751331   3.951202                     -.9928825    14.49554
       /cut2 |   8.150515   3.975476                      .3587254     15.9423
------------------------------------------------------------------------------
Note: 22 observations completely determined.  Standard errors questionable.

. 
. *store estimates
. est sto ordv1_2

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     ordv1_2 |        597 -185.3092  -137.7404      32    339.4809   480.0222
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=339.4809
. 
. *Model 3: Ano
. ologit ord1 seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2* if durable2==1, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Ordered logistic regression                     Number of obs     =      1,067
                                                Wald chi2(30)     =     304.97
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -378.94747               Pseudo R2         =     0.2530

                                 (Std. Err. adjusted for 86 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
        ord1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.5626958   .1431194    -3.93   0.000    -.8432046   -.2821869
     polity2 |    .069583   .0464115     1.50   0.134    -.0213819     .160548
  public_cor |   .5646743   .9042607     0.62   0.532    -1.207644    2.336993
 physical_vd |  -4.257759    1.21959    -3.49   0.000     -6.64811   -1.867407
  express_vd |   4.257182   1.179945     3.61   0.000     1.944531    6.569832
  intensity2 |   1.321333   .1996017     6.62   0.000     .9301205    1.712545
        info |    .038828   .0096208     4.04   0.000     .0199716    .0576843
      pop_ln |   .2812936   .1426367     1.97   0.049     .0017307    .5608565
       y1992 |  -.1304146   .7279667    -0.18   0.858    -1.557203    1.296374
       y1993 |   .3777225   .6492725     0.58   0.561    -.8948282    1.650273
       y1994 |   .7694224   .6319656     1.22   0.223    -.4692074    2.008052
       y1995 |   .6922076   .6218385     1.11   0.266    -.5265734    1.910989
       y1996 |  -.4654113   .8697441    -0.54   0.593    -2.170078    1.239256
       y1997 |  -1.244756   .7335868    -1.70   0.090     -2.68256    .1930476
       y1998 |  -.1470673   .8007196    -0.18   0.854    -1.716449    1.422314
       y1999 |  -.6125212   .7841953    -0.78   0.435    -2.149516    .9244733
       y2000 |  -.3003775   .6556167    -0.46   0.647    -1.585363    .9846076
       y2001 |  -.7490837   .8544569    -0.88   0.381    -2.423788    .9256211
       y2002 |  -1.347029   .9132961    -1.47   0.140    -3.137057    .4429982
       y2003 |  -.1728648    .610889    -0.28   0.777    -1.370185    1.024456
       y2004 |   -.254357   .7074411    -0.36   0.719    -1.640916    1.132202
       y2005 |   .2928285   .6921832     0.42   0.672    -1.063826    1.649483
       y2006 |  -.5595887   .6204114    -0.90   0.367    -1.775573    .6563952
       y2007 |  -.2970966   .6248259    -0.48   0.634    -1.521733    .9275397
       y2008 |   -.199708   .6822596    -0.29   0.770    -1.536912    1.137496
       y2009 |    .002429     .61072     0.00   0.997     -1.19456    1.199418
       y2010 |   .4535531   .6625797     0.68   0.494    -.8450792    1.752185
       y2011 |   .0201275   .5018015     0.04   0.968    -.9633854     1.00364
       y2012 |   -.196121   .5846339    -0.34   0.737    -1.341982    .9497403
       y2013 |  -.0368585   .6122388    -0.06   0.952    -1.236824    1.163108
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
-------------+----------------------------------------------------------------
       /cut1 |   8.603164    2.49664                      3.709841    13.49649
       /cut2 |     9.9092   2.522668                      4.964861    14.85354
------------------------------------------------------------------------------

. 
. *store estimates
. est sto ordv1_3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     ordv1_3 |      1,067 -507.2613  -378.9475      32    821.8949   981.0183
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=821.8949
. 
. *Model 4: Demo
. ologit ord1 seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2* if durable2==2, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Ordered logistic regression                     Number of obs     =      1,922
                                                Wald chi2(30)     =     821.69
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -491.50374               Pseudo R2         =     0.3719

                                (Std. Err. adjusted for 106 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
        ord1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |    .060431   .1234444     0.49   0.624    -.1815156    .3023776
     polity2 |  -.3589208   .1589447    -2.26   0.024    -.6704468   -.0473949
  public_cor |    1.58187   .9495571     1.67   0.096    -.2792273    3.442968
 physical_vd |  -7.448605    1.40347    -5.31   0.000    -10.19936   -4.697854
  express_vd |    5.77731   1.601521     3.61   0.000     2.638387    8.916233
  intensity2 |   .9391871   .2324094     4.04   0.000      .483673    1.394701
        info |   .0390972   .0101673     3.85   0.000     .0191696    .0590248
      pop_ln |   .6481521   .1221907     5.30   0.000     .4086627    .8876415
       y1992 |   .4775249   .8353233     0.57   0.568    -1.159679    2.114729
       y1993 |   1.325906   .7455641     1.78   0.075    -.1353733    2.787184
       y1994 |   .1017085   .5936692     0.17   0.864    -1.061862    1.265279
       y1995 |   .0184487   .6856574     0.03   0.979    -1.325415    1.362312
       y1996 |   .1581828   .7337575     0.22   0.829    -1.279955    1.596321
       y1997 |   .3815246   .6392009     0.60   0.551    -.8712862    1.634335
       y1998 |   .0572161   .7243804     0.08   0.937    -1.362543    1.476975
       y1999 |  -1.245531   .8996473    -1.38   0.166    -3.008807    .5177455
       y2000 |   .2898388   .6483956     0.45   0.655    -.9809933    1.560671
       y2001 |   .9785483   .5975219     1.64   0.101    -.1925731     2.14967
       y2002 |   -.091682   .5668443    -0.16   0.872    -1.202676    1.019312
       y2003 |   .1086883   .5992958     0.18   0.856     -1.06591    1.283287
       y2004 |   .5731955     .50074     1.14   0.252    -.4082369    1.554628
       y2005 |  -.0914832    .611945    -0.15   0.881    -1.290873    1.107907
       y2006 |  -.3913332   .6416186    -0.61   0.542    -1.648883    .8662161
       y2007 |   .2348078   .4829649     0.49   0.627    -.7117859    1.181402
       y2008 |   .0455038   .5457748     0.08   0.934    -1.024195    1.115203
       y2009 |   .1161135   .5396611     0.22   0.830    -.9416028     1.17383
       y2010 |   .0545918   .6008178     0.09   0.928    -1.122989    1.232173
       y2011 |  -.0214016   .5653204    -0.04   0.970    -1.129409    1.086606
       y2012 |   .0463605   .3975165     0.12   0.907    -.7327574    .8254785
       y2013 |  -.8318598    .616462    -1.35   0.177    -2.040103    .3763834
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
-------------+----------------------------------------------------------------
       /cut1 |   13.06219   2.697333                      7.775512    18.34886
       /cut2 |   14.44203   2.670014                      9.208899    19.67516
------------------------------------------------------------------------------

. 
. *store estimates
. est sto ordv1_4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     ordv1_4 |      1,922  -782.527  -491.5037      32    1047.007   1224.963
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1047.007
. 
. ****Create TABLE 13 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab ordv1_1 ordv1_2 ordv1_3 ordv1_4 using apx_ord1.tex, replace se aic obs
> last r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" unconfirmed "CPJ Unconfirm
> ed") ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(output written to apx_ord1.tex)

. 
. *Table 14: Ordinal 2
. 
. *Model 1: Global
. ologit ord2 seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2*, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Ordered logistic regression                     Number of obs     =      3,586
                                                Wald chi2(30)     =     514.85
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -878.42188               Pseudo R2         =     0.3067

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
        ord2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3151004    .083835    -3.76   0.000    -.4794141   -.1507868
     polity2 |    .043396   .0308328     1.41   0.159    -.0170351    .1038271
  public_cor |   1.369234   .5707257     2.40   0.016     .2506322    2.487836
 physical_vd |  -4.648857    .924357    -5.03   0.000    -6.460564   -2.837151
  express_vd |   3.765387   .8663006     4.35   0.000     2.067469    5.463305
  intensity2 |   1.295708   .1337118     9.69   0.000     1.033637    1.557778
        info |   .0335762   .0064507     5.21   0.000     .0209331    .0462193
      pop_ln |   .4691157   .0760521     6.17   0.000     .3200563     .618175
       y1992 |   .1890504   .4574683     0.41   0.679     -.707571    1.085672
       y1993 |   .5998501   .4431902     1.35   0.176    -.2687868    1.468487
       y1994 |    .610819    .411215     1.49   0.137    -.1951475    1.416786
       y1995 |   .3689421   .4667481     0.79   0.429    -.5458674    1.283752
       y1996 |  -.0669912    .469239    -0.14   0.886    -.9866828    .8527004
       y1997 |  -.1263802   .4219735    -0.30   0.765    -.9534331    .7006727
       y1998 |    .082753   .4446679     0.19   0.852    -.7887801    .9542861
       y1999 |  -.7844594   .5288335    -1.48   0.138    -1.820954    .2520353
       y2000 |  -.0690249   .4060022    -0.17   0.865    -.8647746    .7267248
       y2001 |   .3391657   .4039586     0.84   0.401    -.4525787     1.13091
       y2002 |   -.679103   .3830274    -1.77   0.076    -1.429823    .0716169
       y2003 |    .063456   .3899761     0.16   0.871    -.7008831    .8277952
       y2004 |   .1949974   .3580932     0.54   0.586    -.5068524    .8968471
       y2005 |   .0186118   .3886964     0.05   0.962    -.7432191    .7804428
       y2006 |  -.3752773   .4364936    -0.86   0.390    -1.230789    .4802343
       y2007 |   .0952722   .3703685     0.26   0.797    -.6306368    .8211811
       y2008 |  -.5282958   .4088702    -1.29   0.196    -1.329667     .273075
       y2009 |   .2701654   .4073023     0.66   0.507    -.5281324    1.068463
       y2010 |   .1239153   .4064794     0.30   0.760    -.6727697    .9206003
       y2011 |   .0649468   .3646789     0.18   0.859    -.6498108    .7797044
       y2012 |   .2248971   .3595504     0.63   0.532    -.4798087    .9296029
       y2013 |  -.4094576   .3791758    -1.08   0.280    -1.152628    .3337133
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
-------------+----------------------------------------------------------------
       /cut1 |   12.21888    1.50878                      9.261725    15.17603
       /cut2 |   16.79772   1.761168                      13.34589    20.24954
------------------------------------------------------------------------------

. 
. *store estimates
. est sto ordv2_1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     ordv2_1 |      3,586 -1266.927  -878.4219      32    1820.844   2018.757
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1820.844
. 
. *Model 2: Auto
. ologit ord2 seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2* if durable2==0, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Ordered logistic regression                     Number of obs     =        597
                                                Wald chi2(30)     =   10288.35
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -124.68872               Pseudo R2         =     0.2778

                                 (Std. Err. adjusted for 52 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
        ord2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4560771   .2161076    -2.11   0.035    -.8796403   -.0325139
     polity2 |    .014595   .3092129     0.05   0.962    -.5914511    .6206411
  public_cor |  -.2457516   1.587965    -0.15   0.877    -3.358106    2.866603
 physical_vd |  -2.449225    1.88291    -1.30   0.193    -6.139661    1.241211
  express_vd |   2.358826   2.567616     0.92   0.358     -2.67361    7.391261
  intensity2 |   1.787096   .4538872     3.94   0.000     .8974938    2.676699
        info |   .0371989   .0145667     2.55   0.011     .0086487    .0657492
      pop_ln |   .2010857   .1439088     1.40   0.162    -.0809704    .4831417
       y1992 |  -.1927475   1.300772    -0.15   0.882    -2.742215     2.35672
       y1993 |  -.3586643   1.137706    -0.32   0.753    -2.588527    1.871199
       y1994 |   1.102476   1.153494     0.96   0.339     -1.15833    3.363283
       y1995 |  -.5563904   1.554868    -0.36   0.720    -3.603876    2.491095
       y1996 |  -.8224482   1.438492    -0.57   0.567    -3.641841    1.996945
       y1997 |  -.9285861   1.443803    -0.64   0.520    -3.758387    1.901215
       y1998 |  -.3841529   1.332708    -0.29   0.773    -2.996212    2.227906
       y1999 |  -.5826529   1.360861    -0.43   0.669    -3.249891    2.084585
       y2000 |  -1.025629   1.542277    -0.67   0.506    -4.048436    1.997178
       y2001 |  -.5981666   1.794219    -0.33   0.739    -4.114772    2.918439
       y2002 |  -1.650318   1.271441    -1.30   0.194    -4.142297    .8416602
       y2003 |  -.2275303   1.439842    -0.16   0.874    -3.049569    2.594508
       y2004 |  -.3470198   1.312694    -0.26   0.792    -2.919853    2.225813
       y2005 |   .1946983   1.295747     0.15   0.881    -2.344918    2.734315
       y2006 |   .3536604   1.457509     0.24   0.808    -2.503005    3.210326
       y2007 |   .1790857   1.244749     0.14   0.886    -2.260578    2.618749
       y2008 |  -14.93331   1.020464   -14.63   0.000    -16.93338   -12.93323
       y2009 |   .8420596   1.311929     0.64   0.521    -1.729274    3.413394
       y2010 |  -.6122627     1.7278    -0.35   0.723    -3.998688    2.774163
       y2011 |   .4716943   1.118004     0.42   0.673    -1.719553    2.662942
       y2012 |   .7758108   .7623556     1.02   0.309    -.7183786        2.27
       y2013 |   .1975999   .2351031     0.84   0.401    -.2631938    .6583936
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
-------------+----------------------------------------------------------------
       /cut1 |   6.755471   3.938483                     -.9638138    14.47476
       /cut2 |   9.428739   3.978945                      1.630151    17.22733
------------------------------------------------------------------------------
Note: 22 observations completely determined.  Standard errors questionable.

. 
. *store estimates
. est sto ordv2_2

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     ordv2_2 |        597 -172.6554  -124.6887      32    313.3774   453.9188
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=313.3774
. 
. *Model 3: Ano
. ologit ord2 seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2* if durable2==1, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Ordered logistic regression                     Number of obs     =      1,067
                                                Wald chi2(30)     =     260.57
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -313.62309               Pseudo R2         =     0.2723

                                 (Std. Err. adjusted for 86 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
        ord2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.5240767   .1389299    -3.77   0.000    -.7963744    -.251779
     polity2 |    .068706     .04423     1.55   0.120    -.0179832    .1553953
  public_cor |   .4649857   .8458587     0.55   0.583    -1.192867    2.122838
 physical_vd |  -4.130029   1.230957    -3.36   0.001     -6.54266   -1.717398
  express_vd |   3.983447   1.161021     3.43   0.001     1.707887    6.259007
  intensity2 |    1.24346   .1773726     7.01   0.000     .8958161    1.591104
        info |   .0342671   .0091995     3.72   0.000     .0162365    .0522978
      pop_ln |   .2771945   .1317965     2.10   0.035     .0188781    .5355109
       y1992 |  -.3291473   .7409139    -0.44   0.657    -1.781312    1.123017
       y1993 |  -.0400237    .591767    -0.07   0.946    -1.199866    1.119818
       y1994 |   .5889486   .6247361     0.94   0.346    -.6355117    1.813409
       y1995 |   .6458568   .6627391     0.97   0.330     -.653088    1.944802
       y1996 |   -.769559   .8608067    -0.89   0.371    -2.456709    .9175911
       y1997 |  -1.279456   .7916419    -1.62   0.106    -2.831045    .2721339
       y1998 |  -.2237569   .7611211    -0.29   0.769    -1.715527    1.268013
       y1999 |  -.8495955   .7882996    -1.08   0.281    -2.394634    .6954434
       y2000 |    -.41407   .6972872    -0.59   0.553    -1.780728    .9525879
       y2001 |  -.8739687   .8700596    -1.00   0.315    -2.579254    .8313168
       y2002 |  -1.466369   .8998979    -1.63   0.103    -3.230136    .2973984
       y2003 |  -.1771123   .6764509    -0.26   0.793    -1.502932    1.148707
       y2004 |  -.2163424   .7345037    -0.29   0.768    -1.655943    1.223258
       y2005 |   .0408244   .6903384     0.06   0.953    -1.312214    1.393863
       y2006 |  -.6465542   .6934855    -0.93   0.351    -2.005761    .7126523
       y2007 |   -.216844    .650638    -0.33   0.739    -1.492071    1.058383
       y2008 |  -.6332439   .7095887    -0.89   0.372    -2.024012    .7575244
       y2009 |  -.2326964   .6995432    -0.33   0.739    -1.603776    1.138383
       y2010 |   .1483092   .6813417     0.22   0.828    -1.187096    1.483714
       y2011 |  -.1066017   .5921744    -0.18   0.857    -1.267242    1.054039
       y2012 |   .0002934   .6753611     0.00   1.000     -1.32339    1.323977
       y2013 |  -.1318868   .6076861    -0.22   0.828     -1.32293    1.059156
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
-------------+----------------------------------------------------------------
       /cut1 |   8.085692   2.196802                       3.78004    12.39134
       /cut2 |   12.55676   2.484071                       7.68807    17.42545
------------------------------------------------------------------------------

. 
. *store estimates
. est sto ordv2_3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     ordv2_3 |      1,067 -430.9601  -313.6231      32    691.2462   850.3696
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=691.2462
. 
. *Model 4: Demo
. ologit ord2 seq_ln polity public_cor physical_vd express_vd intensity2 info p
> op_ln ///
>   y199* y2* if durable2==2, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Ordered logistic regression                     Number of obs     =      1,922
                                                Wald chi2(30)     =     626.65
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -373.24327               Pseudo R2         =     0.4260

                                (Std. Err. adjusted for 106 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
        ord2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   .1020992   .1239829     0.82   0.410    -.1409028    .3451012
     polity2 |  -.3286618   .1561052    -2.11   0.035    -.6346224   -.0227012
  public_cor |   1.899156    .853233     2.23   0.026     .2268495    3.571462
 physical_vd |  -7.121349   1.362938    -5.23   0.000    -9.792658   -4.450041
  express_vd |   5.519776   1.518155     3.64   0.000     2.544248    8.495304
  intensity2 |   1.033197   .2082777     4.96   0.000     .6249801    1.441414
        info |   .0395731   .0103346     3.83   0.000     .0193176    .0598286
      pop_ln |   .6339285   .1158161     5.47   0.000     .4069331    .8609239
       y1992 |   .5946743   .8421084     0.71   0.480    -1.055828    2.245176
       y1993 |   1.610441   .7705117     2.09   0.037     .1002661    3.120617
       y1994 |   .3131949   .6454784     0.49   0.628    -.9519195    1.578309
       y1995 |   .3266257   .7197147     0.45   0.650    -1.083989    1.737241
       y1996 |   .7825139   .7435703     1.05   0.293    -.6748571    2.239885
       y1997 |   .7571991   .6514823     1.16   0.245    -.5196827    2.034081
       y1998 |   .1594535   .7575647     0.21   0.833    -1.325346    1.644253
       y1999 |   -1.16485   .8962008    -1.30   0.194    -2.921371    .5916714
       y2000 |   .4716723   .6470662     0.73   0.466    -.7965541    1.739899
       y2001 |   1.402293   .5721664     2.45   0.014     .2808672    2.523718
       y2002 |  -.1085571   .5647647    -0.19   0.848    -1.215475    .9983613
       y2003 |   .1851718   .6058841     0.31   0.760    -1.002339    1.372683
       y2004 |   .6370459    .559873     1.14   0.255    -.4602849    1.734377
       y2005 |  -.1162115     .61462    -0.19   0.850    -1.320845    1.088421
       y2006 |  -.3139681   .6474313    -0.48   0.628     -1.58291    .9549739
       y2007 |   .4289123    .510414     0.84   0.401    -.5714807    1.429305
       y2008 |  -.0117476   .5524474    -0.02   0.983    -1.094525    1.071029
       y2009 |   .6186316    .573259     1.08   0.281    -.5049354    1.742199
       y2010 |   .2943162   .5676897     0.52   0.604    -.8183351    1.406968
       y2011 |   .1732379   .5593832     0.31   0.757    -.9231331    1.269609
       y2012 |    .272199   .4912894     0.55   0.580    -.6907105    1.235108
       y2013 |  -.9319983    .605177    -1.54   0.124    -2.118123    .2541269
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
-------------+----------------------------------------------------------------
       /cut1 |   13.61081   2.602732                      8.509548    18.71207
       /cut2 |    20.3804   2.719135                      15.05099     25.7098
------------------------------------------------------------------------------

. 
. *store estimates 
. est sto ordv2_4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
     ordv2_4 |      1,922  -650.225  -373.2433      32    810.4865   988.4424
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=810.4865
. 
. ****Create TABLE 14 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab ordv2_1 ordv2_2 ordv2_3 ordv2_4 using apx_ord2.tex, replace se aic obs
> last r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" unconfirmed "CPJ Unconfirm
> ed") ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(output written to apx_ord2.tex)

. 
. *Table 15: ZINB
. 
. *Model 1: Global
. zinb confirmed seq_ln polity public_cor physical_vd express_vd intensity2 inf
> o pop_ln ///
>   y199* y2* , inflate(pop_ln fix) cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Zero-inflated negative binomial regression      Number of obs     =      3,586
                                                Nonzero obs       =        384
                                                Zero obs          =      3,202

Inflation model      = logit                    Wald chi2(30)     =     312.52
Log pseudolikelihood = -1370.113                Prob > chi2       =     0.0000

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
confirmed    |
      seq_ln |  -.2476076   .0827183    -2.99   0.003    -.4097326   -.0854826
     polity2 |  -.0065601   .0241828    -0.27   0.786    -.0539576    .0408374
  public_cor |   .3381579   .5195949     0.65   0.515    -.6802294    1.356545
 physical_vd |  -4.237819   .6494217    -6.53   0.000    -5.510663   -2.964976
  express_vd |    3.11373    .611051     5.10   0.000     1.916092    4.311368
  intensity2 |   1.120886   .1354761     8.27   0.000      .855358    1.386415
        info |   .0227325   .0051011     4.46   0.000     .0127344    .0327306
      pop_ln |    .200948   .0722019     2.78   0.005     .0594347    .3424612
       y1992 |   .0500976   .3796598     0.13   0.895     -.694022    .7942171
       y1993 |   .5850247   .3529134     1.66   0.097    -.1066728    1.276722
       y1994 |   .5726519   .3651922     1.57   0.117    -.1431117    1.288416
       y1995 |   .2901279   .3125051     0.93   0.353    -.3223707    .9026266
       y1996 |  -.3144181   .3890846    -0.81   0.419     -1.07701    .4481736
       y1997 |   -.096279   .4439806    -0.22   0.828     -.966465     .773907
       y1998 |  -.2515822   .3304635    -0.76   0.446    -.8992787    .3961142
       y1999 |  -.2364863   .4679963    -0.51   0.613    -1.153742    .6807696
       y2000 |  -.3122088   .3879606    -0.80   0.421    -1.072598      .44818
       y2001 |   .2628127   .3949961     0.67   0.506    -.5113654    1.036991
       y2002 |  -.5612206   .3800111    -1.48   0.140    -1.306029    .1835875
       y2003 |  -.0172863   .3482253    -0.05   0.960    -.6997953    .6652227
       y2004 |   .2677376   .3529454     0.76   0.448    -.4240226    .9594979
       y2005 |  -.0835773   .3559595    -0.23   0.814     -.781245    .6140904
       y2006 |  -.5304031   .3922012    -1.35   0.176    -1.299103    .2382972
       y2007 |   -.110697   .3091037    -0.36   0.720    -.7165291    .4951352
       y2008 |  -.3639298    .381603    -0.95   0.340    -1.111858    .3839984
       y2009 |   .1994274   .4240649     0.47   0.638    -.6317245    1.030579
       y2010 |   .0866585   .3868531     0.22   0.823    -.6715597    .8448766
       y2011 |  -.1013708    .277798    -0.36   0.715    -.6458448    .4431032
       y2012 |   .3345126   .2309789     1.45   0.148    -.1181978    .7872229
       y2013 |   .3023346    .290203     1.04   0.298     -.266453    .8711221
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -5.342689   1.146482    -4.66   0.000    -7.589752   -3.095626
-------------+----------------------------------------------------------------
inflate      |
      pop_ln |  -.4048551   .1219748    -3.32   0.001    -.6439213   -.1657889
         fix |   -7.19893   .3948278   -18.23   0.000    -7.972778   -6.425081
       _cons |   14.14897   2.139772     6.61   0.000     9.955094    18.34285
-------------+----------------------------------------------------------------
    /lnalpha |  -.0148013   .2162956    -0.07   0.945    -.4387329    .4091302
-------------+----------------------------------------------------------------
       alpha |   .9853077   .2131177                       .644853    1.505508
------------------------------------------------------------------------------

. 
. *store estimates 
. est sto zinb1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       zinb1 |      3,586  -1602.48  -1370.113      35    2810.225   3026.693
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=2810.225
. 
. *Model 2: Auto
. zinb confirmed seq_ln polity public_cor physical_vd express_vd intensity2 inf
> o pop_ln ///
>   y199* y2* if durable2==0, inflate(pop_ln fix) cluster(ccode)
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -372.35618  (not concave)
Iteration 1:   log pseudolikelihood = -243.61386  (not concave)
Iteration 2:   log pseudolikelihood = -238.58938  (not concave)
Iteration 3:   log pseudolikelihood =  -237.1147  (not concave)
Iteration 4:   log pseudolikelihood = -235.97217  (not concave)
Iteration 5:   log pseudolikelihood = -234.97627  (not concave)
Iteration 6:   log pseudolikelihood = -234.27685  (not concave)
Iteration 7:   log pseudolikelihood = -233.61816  
Iteration 8:   log pseudolikelihood = -229.10714  
Iteration 9:   log pseudolikelihood = -227.33185  
Iteration 10:  log pseudolikelihood = -227.16703  
Iteration 11:  log pseudolikelihood = -227.15232  
Iteration 12:  log pseudolikelihood = -227.15193  
Iteration 13:  log pseudolikelihood = -227.15193  

Fitting full model:

Iteration 0:   log pseudolikelihood = -227.15193  (not concave)
Iteration 1:   log pseudolikelihood = -208.41884  (not concave)
Iteration 2:   log pseudolikelihood = -188.41388  
Iteration 3:   log pseudolikelihood = -178.09015  
Iteration 4:   log pseudolikelihood =   -171.877  (not concave)
Iteration 5:   log pseudolikelihood = -166.28161  
Iteration 6:   log pseudolikelihood = -163.60526  
Iteration 7:   log pseudolikelihood = -162.45891  
Iteration 8:   log pseudolikelihood = -162.31619  
Iteration 9:   log pseudolikelihood = -162.28851  
Iteration 10:  log pseudolikelihood =  -162.2827  
Iteration 11:  log pseudolikelihood =  -162.2814  
Iteration 12:  log pseudolikelihood = -162.28109  
Iteration 13:  log pseudolikelihood = -162.28102  
Iteration 14:  log pseudolikelihood = -162.28101  

Zero-inflated negative binomial regression      Number of obs     =        597
                                                Nonzero obs       =         44
                                                Zero obs          =        553

Inflation model      = logit                    Wald chi2(30)     = 1685150.58
Log pseudolikelihood =  -162.281                Prob > chi2       =     0.0000

                                 (Std. Err. adjusted for 52 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
confirmed    |
      seq_ln |  -.2641372    .154669    -1.71   0.088    -.5672829    .0390084
     polity2 |  -.0665546   .1085548    -0.61   0.540     -.279318    .1462089
  public_cor |  -2.233061   .9762088    -2.29   0.022    -4.146395   -.3197271
 physical_vd |  -5.013083   1.290202    -3.89   0.000    -7.541832   -2.484333
  express_vd |   3.213721      1.312     2.45   0.014     .6422487    5.785193
  intensity2 |   2.056951    .348923     5.90   0.000     1.373075    2.740828
        info |   .0370571   .0169175     2.19   0.028     .0038995    .0702147
      pop_ln |  -.0223607   .1608233    -0.14   0.889    -.3375687    .2928472
       y1992 |    .161768   .7832983     0.21   0.836    -1.373468    1.697004
       y1993 |   .9065079   1.037649     0.87   0.382    -1.127246    2.940262
       y1994 |   .4383914   .6875207     0.64   0.524    -.9091243    1.785907
       y1995 |  -.3089144   .9332256    -0.33   0.741    -2.138003    1.520174
       y1996 |  -.6851901   1.230714    -0.56   0.578    -3.097346    1.726966
       y1997 |   .3032967     .95268     0.32   0.750    -1.563922    2.170515
       y1998 |  -1.776329    .855892    -2.08   0.038    -3.453847   -.0988118
       y1999 |    .683053   .6588159     1.04   0.300    -.6082024    1.974308
       y2000 |  -.5530849   1.790885    -0.31   0.757    -4.063155    2.956985
       y2001 |  -.1440177   1.567872    -0.09   0.927    -3.216989    2.928954
       y2002 |  -2.439555   .6328062    -3.86   0.000    -3.679832   -1.199277
       y2003 |    -.56598   .8301902    -0.68   0.495    -2.193123    1.061163
       y2004 |  -1.088301   .9567316    -1.14   0.255    -2.963461    .7868581
       y2005 |  -1.078236   .6130328    -1.76   0.079    -2.279758    .1232867
       y2006 |   1.023208   .8546197     1.20   0.231    -.6518153    2.698232
       y2007 |  -.0648671   1.168289    -0.06   0.956    -2.354672    2.224938
       y2008 |  -44.00306   .4801245   -91.65   0.000    -44.94409   -43.06204
       y2009 |  -1.019122   .8584922    -1.19   0.235    -2.701736    .6634921
       y2010 |   .0965088   1.229016     0.08   0.937    -2.312318    2.505335
       y2011 |    .097893   .4621595     0.21   0.832    -.8079229    1.003709
       y2012 |   .5861032   .0653889     8.96   0.000     .4579433    .7142631
       y2013 |   .5498686   .0119364    46.07   0.000     .5264737    .5732635
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -1.018304   3.473932    -0.29   0.769    -7.827087    5.790478
-------------+----------------------------------------------------------------
inflate      |
      pop_ln |  -.2861937   .2265275    -1.26   0.206    -.7301794     .157792
         fix |  -.7950547   .2587032    -3.07   0.002    -1.302104   -.2880057
       _cons |   6.396965    3.91083     1.64   0.102     -1.26812    14.06205
-------------+----------------------------------------------------------------
    /lnalpha |  -16.62797   1.106483   -15.03   0.000    -18.79664    -14.4593
-------------+----------------------------------------------------------------
       alpha |   6.01e-08   6.65e-08                      6.87e-09    5.25e-07
------------------------------------------------------------------------------

. 
. *store estimates 
. est sto zinb2

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       zinb2 |        597 -227.1519   -162.281      35     394.562   548.2791
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=394.562
. 
. *Model 3: Ano
. zinb confirmed seq_ln polity public_cor physical_vd express_vd intensity2 inf
> o pop_ln ///
>   y199* y2* if durable2==1, inflate(pop_ln fix) cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Zero-inflated negative binomial regression      Number of obs     =      1,067
                                                Nonzero obs       =        139
                                                Zero obs          =        928

Inflation model      = logit                    Wald chi2(30)     =     339.05
Log pseudolikelihood = -481.5962                Prob > chi2       =     0.0000

                                 (Std. Err. adjusted for 86 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
confirmed    |
      seq_ln |  -.5640082   .1411785    -4.00   0.000     -.840713   -.2873034
     polity2 |   .0072814   .0414648     0.18   0.861    -.0739881    .0885509
  public_cor |   .3420908   .7155907     0.48   0.633    -1.060441    1.744623
 physical_vd |  -3.789197   1.055271    -3.59   0.000    -5.857491   -1.720903
  express_vd |   3.367696   .8437923     3.99   0.000     1.713893    5.021498
  intensity2 |   1.088352   .1812739     6.00   0.000     .7330621    1.443643
        info |   .0255618   .0074537     3.43   0.001     .0109529    .0401707
      pop_ln |   .1413709   .1347809     1.05   0.294    -.1227947    .4055366
       y1992 |     .29449    .500066     0.59   0.556    -.6856213    1.274601
       y1993 |   .9581789   .4003593     2.39   0.017     .1734891    1.742869
       y1994 |   1.098219   .4131278     2.66   0.008     .2885033    1.907934
       y1995 |   1.004071   .3978586     2.52   0.012     .2242826     1.78386
       y1996 |   .4080927   .4987706     0.82   0.413    -.5694797    1.385665
       y1997 |  -1.308986   .5492265    -2.38   0.017     -2.38545   -.2325214
       y1998 |   .0289261   .5552156     0.05   0.958    -1.059276    1.117129
       y1999 |   .3898666   .5340451     0.73   0.465    -.6568426    1.436576
       y2000 |   .2615277   .5352874     0.49   0.625    -.7876164    1.310672
       y2001 |  -.0201644   .8223092    -0.02   0.980    -1.631861    1.591532
       y2002 |  -.7924335   .6832835    -1.16   0.246    -2.131645    .5467776
       y2003 |    .068127   .5322121     0.13   0.898    -.9749896    1.111244
       y2004 |   .3150322   .6882631     0.46   0.647    -1.033939    1.664003
       y2005 |   .7952768   .5235753     1.52   0.129     -.230912    1.821466
       y2006 |   -.443502   .5028964    -0.88   0.378    -1.429161    .5421568
       y2007 |   .3479908   .4818405     0.72   0.470    -.5963993    1.292381
       y2008 |   .1757372   .4770698     0.37   0.713    -.7593024    1.110777
       y2009 |   .4847419   .4452233     1.09   0.276    -.3878798    1.357364
       y2010 |   .7050845   .5907864     1.19   0.233    -.4528357    1.863005
       y2011 |   .2812721   .3220912     0.87   0.383     -.350015    .9125592
       y2012 |   .3526742   .4299405     0.82   0.412    -.4899937    1.195342
       y2013 |   .9455263   .4622206     2.05   0.041     .0395905    1.851462
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -4.899099   1.954935    -2.51   0.012      -8.7307   -1.067498
-------------+----------------------------------------------------------------
inflate      |
      pop_ln |  -.2991869   .3016577    -0.99   0.321    -.8904251    .2920514
         fix |  -6.965787   .5052793   -13.79   0.000    -7.956116   -5.975457
       _cons |   11.74139   5.377258     2.18   0.029      1.20216    22.28062
-------------+----------------------------------------------------------------
    /lnalpha |  -.3172937   .3662417    -0.87   0.386    -1.035114    .4005268
-------------+----------------------------------------------------------------
       alpha |   .7281169   .2666667                      .3551858    1.492611
------------------------------------------------------------------------------

. 
. *store estimates 
. est sto zinb3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       zinb3 |      1,067 -580.5628  -481.5962      35    1033.192   1207.234
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1033.192
. 
. *Model 4: Demo
. zinb confirmed seq_ln polity public_cor physical_vd express_vd intensity2 inf
> o pop_ln ///
>   y199* y2* if durable2==2, inflate(pop_ln fix) cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Zero-inflated negative binomial regression      Number of obs     =      1,922
                                                Nonzero obs       =        201
                                                Zero obs          =      1,721

Inflation model      = logit                    Wald chi2(30)     =     425.34
Log pseudolikelihood = -632.4929                Prob > chi2       =     0.0000

                                (Std. Err. adjusted for 106 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
confirmed    |
      seq_ln |   .0438838   .0770981     0.57   0.569    -.1072257    .1949934
     polity2 |  -.2802145   .1225761    -2.29   0.022    -.5204591   -.0399698
  public_cor |   .5127912   .5726348     0.90   0.371    -.6095523    1.635135
 physical_vd |  -5.613073   .8358837    -6.72   0.000    -7.251375   -3.974771
  express_vd |   3.917087   1.034481     3.79   0.000     1.889542    5.944633
  intensity2 |   .5396937   .1328426     4.06   0.000     .2793271    .8000604
        info |   .0172532   .0054769     3.15   0.002     .0065187    .0279876
      pop_ln |   .3898602   .0938371     4.15   0.000     .2059429    .5737776
       y1992 |   .2568641   .5898355     0.44   0.663    -.8991923    1.412921
       y1993 |   .3697528   .5067884     0.73   0.466    -.6235342     1.36304
       y1994 |  -.5326723   .5194498    -1.03   0.305    -1.550775    .4854307
       y1995 |  -.0725971   .4960164    -0.15   0.884    -1.044771    .8995771
       y1996 |  -.4538049   .5720903    -0.79   0.428    -1.575081    .6674714
       y1997 |   .2648057   .5967243     0.44   0.657    -.9047524    1.434364
       y1998 |   -.242876   .4491248    -0.54   0.589    -1.123144    .6373925
       y1999 |  -.9170189   .6177036    -1.48   0.138    -2.127696    .2936578
       y2000 |  -.3092143   .4656241    -0.66   0.507    -1.221821    .6033922
       y2001 |   .3468934   .4649749     0.75   0.456    -.5644407    1.258228
       y2002 |  -.3245981   .3574461    -0.91   0.364     -1.02518    .3759833
       y2003 |   .0442914   .4429307     0.10   0.920    -.8238368    .9124197
       y2004 |   .2717021   .4587166     0.59   0.554     -.627366     1.17077
       y2005 |  -.3101966   .4824026    -0.64   0.520    -1.255688    .6352951
       y2006 |  -.3721312   .4981447    -0.75   0.455    -1.348477    .6042144
       y2007 |  -.0256152   .4050555    -0.06   0.950    -.8195094     .768279
       y2008 |  -.0575347   .4987175    -0.12   0.908    -1.035003    .9199337
       y2009 |   .4858262   .6384946     0.76   0.447    -.7656003    1.737253
       y2010 |  -.1383685   .4832572    -0.29   0.775    -1.085535    .8087982
       y2011 |     -.2216   .3973445    -0.56   0.577    -1.000381    .5571809
       y2012 |  -.1661165   .3320474    -0.50   0.617    -.8169175    .4846845
       y2013 |  -.3376881   .4623412    -0.73   0.465     -1.24386     .568484
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -6.344248   1.780072    -3.56   0.000    -9.833126    -2.85537
-------------+----------------------------------------------------------------
inflate      |
      pop_ln |  -.3524667   .1491331    -2.36   0.018    -.6447622   -.0601711
         fix |  -7.277801   .5848184   -12.44   0.000    -8.424024   -6.131578
       _cons |   13.04423   2.837988     4.60   0.000      7.48188    18.60659
-------------+----------------------------------------------------------------
    /lnalpha |  -.8672688   .2927802    -2.96   0.003    -1.441107   -.2934302
-------------+----------------------------------------------------------------
       alpha |   .4200974   .1229962                      .2366655    .7457012
------------------------------------------------------------------------------

. 
. *store estimates 
. est sto zinb4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       zinb4 |      1,922 -757.1218  -632.4929      35    1334.986   1529.625
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1334.986
. 
. ****Create TABLE 15 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab zinb1 zinb2 zinb3 zinb4 using apx_zinb.tex, replace se aic obslast r2 
> ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" fix "CPJ Unconfirmed") ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(output written to apx_zinb.tex)

. 
. *Table 16: LDV Logit
. 
. *Set for time series to activate lag command (l.) 
. tsset ccode year
       panel variable:  ccode (unbalanced)
        time variable:  year, 1992 to 2016
                delta:  1 unit

. 
. *Model 1: Full
. nbreg confirmed l.confirmed seq_ln polity public_cor physical_vd express_vd i
> ntensity2 info pop_ln   ///
>   y199* y2* , cluster(ccode)  nolog 
note: y1992 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      3,430
                                                Wald chi2(30)     =     825.32
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -1315.4252               Pseudo R2         =     0.2437

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   confirmed |
         L1. |   .1666928   .0462095     3.61   0.000     .0761238    .2572618
             |
      seq_ln |  -.2516412   .0649324    -3.88   0.000    -.3789064   -.1243761
     polity2 |   .0216391   .0232025     0.93   0.351     -.023837    .0671153
  public_cor |   .5196817   .5214145     1.00   0.319     -.502272    1.541635
 physical_vd |  -4.471834   .7260968    -6.16   0.000    -5.894958   -3.048711
  express_vd |   3.109336   .6321458     4.92   0.000     1.870352    4.348319
  intensity2 |   1.031759   .1354256     7.62   0.000     .7663297    1.297188
        info |   .0230226   .0048638     4.73   0.000     .0134897    .0325555
      pop_ln |   .3771181    .066001     5.71   0.000     .2477585    .5064776
       y1992 |          0  (omitted)
       y1993 |   .7170156   .4376662     1.64   0.101    -.1407944    1.574826
       y1994 |   .7717779   .4625038     1.67   0.095    -.1347128    1.678269
       y1995 |   .0858901   .3945677     0.22   0.828    -.6874483    .8592286
       y1996 |  -.5265039   .4720327    -1.12   0.265    -1.451671    .3986632
       y1997 |   -.061222   .4321746    -0.14   0.887    -.9082686    .7858246
       y1998 |  -.0623667    .382096    -0.16   0.870    -.8112611    .6865276
       y1999 |   .0582626   .5122107     0.11   0.909    -.9456519    1.062177
       y2000 |  -.2395279   .4127903    -0.58   0.562    -1.048582    .5695261
       y2001 |   .2995345   .3927046     0.76   0.446    -.4701522    1.069221
       y2002 |  -.4280832   .3502608    -1.22   0.222    -1.114582    .2584154
       y2003 |   .0643235   .3592217     0.18   0.858    -.6397381     .768385
       y2004 |    .365339   .3834725     0.95   0.341    -.3862533    1.116931
       y2005 |   .0095214   .4011579     0.02   0.981    -.7767336    .7957765
       y2006 |   -.433451   .4085472    -1.06   0.289    -1.234189    .3672867
       y2007 |    .194999   .3260206     0.60   0.550    -.4439897    .8339876
       y2008 |  -.2532068   .4100062    -0.62   0.537    -1.056804    .5503906
       y2009 |   .3181549   .3977517     0.80   0.424    -.4614241    1.097734
       y2010 |   .1927122   .4267611     0.45   0.652    -.6437242    1.029149
       y2011 |   .1110629   .3499779     0.32   0.751    -.5748812    .7970069
       y2012 |   .4614739   .4108452     1.12   0.261    -.3437679    1.266716
       y2013 |  -.0454068   .4316513    -0.11   0.916    -.8914279    .8006143
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.232894   1.252219    -7.37   0.000     -11.6872    -6.77859
-------------+----------------------------------------------------------------
    /lnalpha |   .5490635   .2128041                      .1319751    .9661519
-------------+----------------------------------------------------------------
       alpha |   1.731631   .3684981                       1.14108    2.627813
------------------------------------------------------------------------------

. 
. *store estimates 
. est sto lag1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        lag1 |      3,430 -1739.386  -1315.425      32     2694.85   2891.341
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=2694.867
. 
. *Model 2: Auto
. *nbreg confirmed l.confirmed seq_ln polity public_cor physical_vd express_vd 
> intensity2 info pop_ln  ///
>   *  y199* y2* if durable2==0, cluster(ccode) 
. 
.   *maximum likelihood estimator is not concave and fails to produce a
. * maximum likelihood estimate   
. 
. *Model 3: Ano
. nbreg confirmed l.confirmed seq_ln polity public_cor physical_vd express_vd i
> ntensity2 info pop_ln  ///
>   y199* y2* if durable2==1, cluster(ccode)  nolog
note: y1992 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,023
                                                Wald chi2(30)     =     826.13
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -452.69421               Pseudo R2         =     0.2488

                                 (Std. Err. adjusted for 84 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   confirmed |
         L1. |   .1686498   .0315712     5.34   0.000     .1067714    .2305282
             |
      seq_ln |  -.4576296   .1188096    -3.85   0.000    -.6904922   -.2247671
     polity2 |   .0456426   .0371966     1.23   0.220    -.0272615    .1185466
  public_cor |    .739604   .7352277     1.01   0.314    -.7014158    2.180624
 physical_vd |  -4.185085   .9775108    -4.28   0.000    -6.100971     -2.2692
  express_vd |   3.551204   .8084652     4.39   0.000     1.966641    5.135766
  intensity2 |   .9591889   .1430061     6.71   0.000      .678902    1.239476
        info |   .0259852    .006196     4.19   0.000     .0138412    .0381292
      pop_ln |   .2592096   .0929695     2.79   0.005     .0769928    .4414264
       y1992 |          0  (omitted)
       y1993 |   .8611859   .4250969     2.03   0.043     .0280112     1.69436
       y1994 |    .867593    .423574     2.05   0.041     .0374033    1.697783
       y1995 |   .3777804   .4147272     0.91   0.362      -.43507    1.190631
       y1996 |  -.9533093    .638304    -1.49   0.135    -2.204362    .2977436
       y1997 |  -1.340465   .6402743    -2.09   0.036     -2.59538   -.0855506
       y1998 |   .0428044   .5226692     0.08   0.935    -.9816084    1.067217
       y1999 |     .36276   .5120455     0.71   0.479    -.6408307    1.366351
       y2000 |  -.1813282   .4761152    -0.38   0.703    -1.114497    .7518404
       y2001 |  -.2940771   .7473183    -0.39   0.694    -1.758794     1.17064
       y2002 |  -.8346388   .6772771    -1.23   0.218    -2.162077    .4927999
       y2003 |  -.0923447   .4864168    -0.19   0.849    -1.045704    .8610147
       y2004 |   .2244076   .6316734     0.36   0.722     -1.01365    1.462465
       y2005 |   .4872059   .4961347     0.98   0.326    -.4852003    1.459612
       y2006 |  -.4610899   .4653598    -0.99   0.322    -1.373178    .4509985
       y2007 |   .2831809   .4683448     0.60   0.545    -.6347581     1.20112
       y2008 |  -.1702997   .5068301    -0.34   0.737    -1.163668     .823069
       y2009 |    .312965   .4523352     0.69   0.489    -.5735958    1.199526
       y2010 |   .3725562   .5568801     0.67   0.503    -.7189087    1.464021
       y2011 |   .2855835   .3548525     0.80   0.421    -.4099146    .9810816
       y2012 |   .1032635    .414919     0.25   0.803    -.7099627    .9164898
       y2013 |   .6532977   .5117845     1.28   0.202    -.3497816    1.656377
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -7.701028   1.274247    -6.04   0.000    -10.19851   -5.203549
-------------+----------------------------------------------------------------
    /lnalpha |   .0997049   .2907846                     -.4702225    .6696324
-------------+----------------------------------------------------------------
       alpha |   1.104845   .3212719                      .6248632    1.953519
------------------------------------------------------------------------------

. 
. *store estimates 
. est sto lag3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        lag3 |      1,023 -602.6507  -452.6942      32    969.3884   1127.164
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=969.388
. 
. *Model 4: Demo
. nbreg confirmed l.confirmed seq_ln polity public_cor physical_vd express_vd i
> ntensity2 info pop_ln  ///
>   y199* y2* if durable2==2, cluster(ccode)  nolog
note: y1992 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,847
                                                Wald chi2(30)     =     628.04
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -620.00194               Pseudo R2         =     0.3089

                                (Std. Err. adjusted for 106 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   confirmed |
         L1. |   .0695654   .0625033     1.11   0.266    -.0529387    .1920695
             |
      seq_ln |    .079346   .0876744     0.91   0.365    -.0924927    .2511847
     polity2 |   -.340287   .1382056    -2.46   0.014    -.6111651    -.069409
  public_cor |   .6670658   .6971835     0.96   0.339    -.6993888     2.03352
 physical_vd |  -5.899017   .9345256    -6.31   0.000    -7.730654   -4.067381
  express_vd |   4.109639   1.278445     3.21   0.001     1.603933    6.615345
  intensity2 |    .525038   .1745686     3.01   0.003       .18289    .8671861
        info |   .0220755    .006181     3.57   0.000      .009961      .03419
      pop_ln |   .5340934   .0887962     6.01   0.000      .360056    .7081308
       y1992 |          0  (omitted)
       y1993 |   .4867455   .5739162     0.85   0.396    -.6381096    1.611601
       y1994 |   -.409229   .5704121    -0.72   0.473    -1.527216    .7087583
       y1995 |  -.0388029   .5394674    -0.07   0.943     -1.09614    1.018534
       y1996 |  -.2711935   .6000492    -0.45   0.651    -1.447268    .9048814
       y1997 |   .2806768   .6091434     0.46   0.645    -.9132224    1.474576
       y1998 |  -.3221491   .5104347    -0.63   0.528    -1.322583    .6782845
       y1999 |  -.9783277   .6533907    -1.50   0.134     -2.25895    .3022945
       y2000 |  -.2268382   .5073362    -0.45   0.655    -1.221199    .7675225
       y2001 |   .4527456   .4790892     0.95   0.345     -.486252    1.391743
       y2002 |  -.3080715   .3907331    -0.79   0.430    -1.073894    .4577513
       y2003 |   .0034744   .4722758     0.01   0.994    -.9221692    .9291179
       y2004 |   .3014054   .4752383     0.63   0.526    -.6300446    1.232855
       y2005 |  -.4023765   .5205783    -0.77   0.440    -1.422691    .6179382
       y2006 |  -.4753602   .5228743    -0.91   0.363    -1.500175    .5494545
       y2007 |   .0829536   .4297298     0.19   0.847    -.7593013    .9252085
       y2008 |  -.0635883   .5184463    -0.12   0.902    -1.079724    .9525479
       y2009 |   .4497286   .5870559     0.77   0.444    -.7008798    1.600337
       y2010 |  -.1747346    .562817    -0.31   0.756    -1.277836    .9283664
       y2011 |  -.2272659   .4432513    -0.51   0.608    -1.096022    .6414907
       y2012 |  -.1600902   .3643832    -0.44   0.660    -.8742682    .5540877
       y2013 |   -.494562   .5090028    -0.97   0.331    -1.492189    .5030651
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.291746   2.243412    -4.14   0.000    -13.68875   -4.894739
-------------+----------------------------------------------------------------
    /lnalpha |  -.2825612   .2766106                      -.824708    .2595856
-------------+----------------------------------------------------------------
       alpha |   .7538505    .208523                       .438363    1.296393
------------------------------------------------------------------------------

. 
. *store estimates 
. est sto lag4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        lag4 |      1,847 -897.0947  -620.0019      32    1304.004   1480.686
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1304.015
. 
. ****Create TABLE 16 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab lag1  lag3 lag4 using apx_ldv.tex, replace se aic obslast r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(L.confirmed "Lagged DV" seq_ln "Regime-type Duration (ln)" polity2 
> "Polity Level" public_cor ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" fix "CPJ Unconfirmed") ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(note: file apx_ldv.tex not found)
(output written to apx_ldv.tex)

. 
. **********************************
. **********************************
. ** SECTION F: ROBUSTNESS CHECKS **
. **********************************
. **********************************
. 
. *Table 17: w/ Global Media Freedom (GMF) covariate
. 
. *Model 1: Full (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln  gmf ///
>   y199* y2*, cluster(ccode)  nolog 
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      3,582
                                                Wald chi2(31)     =     627.45
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -1394.9135               Pseudo R2         =     0.2298

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.2969445   .0846413    -3.51   0.000    -.4628384   -.1310507
     polity2 |   .0133261   .0264233     0.50   0.614    -.0384626    .0651148
  public_cor |   .4604949   .5719735     0.81   0.421    -.6605526    1.581542
 physical_vd |  -4.742207   .7433656    -6.38   0.000    -6.199177   -3.285237
  express_vd |   3.575794   .7235163     4.94   0.000     2.157728     4.99386
  intensity2 |   1.328321   .1304192    10.19   0.000     1.072704    1.583938
        info |   .0296404    .005922     5.01   0.000     .0180335    .0412472
      pop_ln |   .3854097   .0703057     5.48   0.000     .2476131    .5232064
         gmf |   .1592147   .1866854     0.85   0.394     -.206682    .5251113
       y1992 |   .0444879   .4051592     0.11   0.913    -.7496095    .8385853
       y1993 |   .6223663   .3848778     1.62   0.106    -.1319803    1.376713
       y1994 |    .683702   .3985383     1.72   0.086    -.0974188    1.464823
       y1995 |   .2168477   .3520469     0.62   0.538    -.4731516    .9068469
       y1996 |  -.4215832   .4209332    -1.00   0.317    -1.246597    .4034307
       y1997 |  -.2953335   .4216991    -0.70   0.484    -1.121849    .5311816
       y1998 |  -.2600793   .3651146    -0.71   0.476    -.9756907     .455532
       y1999 |   -.304146   .5000394    -0.61   0.543    -1.284205    .6759132
       y2000 |  -.3566148   .4113044    -0.87   0.386    -1.162757    .4495271
       y2001 |   .1240082   .4055451     0.31   0.760    -.6708456     .918862
       y2002 |  -.6933772   .4158091    -1.67   0.095    -1.508348    .1215937
       y2003 |  -.1271765   .3602214    -0.35   0.724    -.8331975    .5788445
       y2004 |   .2131314   .3772942     0.56   0.572    -.5263516    .9526145
       y2005 |  -.1363561   .3817021    -0.36   0.721    -.8844785    .6117663
       y2006 |  -.6738362     .41228    -1.63   0.102     -1.48189    .1342178
       y2007 |  -.0546614   .3335795    -0.16   0.870    -.7084652    .5991425
       y2008 |   -.502448   .4050159    -1.24   0.215    -1.296265    .2913685
       y2009 |   .0551957   .3981212     0.14   0.890    -.7251075    .8354989
       y2010 |    .078499   .4164834     0.19   0.851    -.7377936    .8947915
       y2011 |   -.106924   .3084105    -0.35   0.729    -.7113974    .4975494
       y2012 |    .208131   .2593862     0.80   0.422    -.3002567    .7165187
       y2013 |   .1150866   .3067751     0.38   0.708    -.4861815    .7163548
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.948548   1.381883    -7.20   0.000    -12.65699   -7.240108
-------------+----------------------------------------------------------------
    /lnalpha |   .7740231   .2159848                      .3507007    1.197346
-------------+----------------------------------------------------------------
       alpha |   2.168473   .4683572                      1.420062    3.311316
------------------------------------------------------------------------------

. 
. *store estimates 
. est sto gmf1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        gmf1 |      3,582 -1811.211  -1394.913      33    2855.827   3059.888
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=2855.827
. 
. *Model 2: Auto (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln gmf ///
>     y199* y2* if durable2==0, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =        597
                                                Wald chi2(31)     =   18212.67
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -177.50415               Pseudo R2         =     0.2613

                                 (Std. Err. adjusted for 52 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |    -.42418   .2068676    -2.05   0.040     -.829633   -.0187271
     polity2 |   .0552555   .2063392     0.27   0.789    -.3491619    .4596729
  public_cor |  -1.201768   1.173148    -1.02   0.306    -3.501096    1.097561
 physical_vd |  -3.605243   1.436678    -2.51   0.012    -6.421079   -.7894071
  express_vd |   1.983004   1.749839     1.13   0.257    -1.446618    5.412626
  intensity2 |   2.066112   .3212335     6.43   0.000     1.436505    2.695718
        info |   .0385777   .0133978     2.88   0.004     .0123184     .064837
      pop_ln |   .2346477   .1342867     1.75   0.081    -.0285495    .4978448
         gmf |   -.362886   .7568262    -0.48   0.632    -1.846238    1.120466
       y1992 |  -.1761734   .9185596    -0.19   0.848    -1.976517     1.62417
       y1993 |   .4286759   .8796944     0.49   0.626    -1.295493    2.152845
       y1994 |   .7032605   .7654045     0.92   0.358    -.7969048    2.203426
       y1995 |  -.9867785   1.075647    -0.92   0.359    -3.095007     1.12145
       y1996 |  -1.213297   1.377049    -0.88   0.378    -3.912264     1.48567
       y1997 |  -.5617393   .9790359    -0.57   0.566    -2.480614    1.357136
       y1998 |  -1.375354    .931082    -1.48   0.140    -3.200241    .4495331
       y1999 |   .2146727   1.111221     0.19   0.847     -1.96328    2.392626
       y2000 |  -1.493973   1.261264    -1.18   0.236    -3.966005     .978059
       y2001 |  -.6464966   1.699843    -0.38   0.704    -3.978128    2.685135
       y2002 |  -2.672549    .936226    -2.85   0.004    -4.507518   -.8375793
       y2003 |  -.4774311   1.240807    -0.38   0.700    -2.909369    1.954507
       y2004 |  -.6901204   1.481559    -0.47   0.641    -3.593922    2.213682
       y2005 |  -.3431096   1.187915    -0.29   0.773     -2.67138    1.985161
       y2006 |   .3438476   1.104732     0.31   0.756    -1.821388    2.509083
       y2007 |   .6133886   1.359298     0.45   0.652    -2.050786    3.277563
       y2008 |  -20.87803   .5632161   -37.07   0.000    -21.98192   -19.77415
       y2009 |   .4667826   1.062967     0.44   0.661    -1.616594    2.550159
       y2010 |  -.5911064   1.614438    -0.37   0.714    -3.755347    2.573134
       y2011 |   .3565753   .8351241     0.43   0.669    -1.280238    1.993389
       y2012 |   .5710413   .2456966     2.32   0.020     .0894848    1.052598
       y2013 |   .4265282   .0816693     5.22   0.000     .2664594    .5865971
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -4.802019   3.427834    -1.40   0.161    -11.52045    1.916413
-------------+----------------------------------------------------------------
    /lnalpha |   1.175569   .5341344                      .1286853    2.222454
-------------+----------------------------------------------------------------
       alpha |   3.239987   1.730589                      1.137332     9.22995
------------------------------------------------------------------------------

. 
. *store estimates        
. est sto gmf2

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        gmf2 |        597 -240.2823  -177.5042      33    421.0083   565.9416
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=421.008
. 
. *Model 3: Ano (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln  gmf  ///
>   y199* y2* if durable2==1, cluster(ccode)  nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,066
                                                Wald chi2(31)     =     456.66
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -485.68583               Pseudo R2         =     0.2252

                                 (Std. Err. adjusted for 86 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.5898287   .1398505    -4.22   0.000    -.8639307   -.3157267
     polity2 |   .0344968   .0387961     0.89   0.374    -.0415422    .1105357
  public_cor |   .3259639   .7036417     0.46   0.643    -1.053148    1.705076
 physical_vd |  -4.311619   1.079155    -4.00   0.000    -6.426725   -2.196513
  express_vd |   3.791776    .888391     4.27   0.000     2.050561     5.53299
  intensity2 |   1.243101   .1552515     8.01   0.000     .9388133    1.547388
        info |   .0306419   .0078363     3.91   0.000     .0152829    .0460008
      pop_ln |   .2589172    .103762     2.50   0.013     .0555474    .4622869
         gmf |   .0104913   .2514637     0.04   0.967    -.4823686    .5033511
       y1992 |   .0912787   .5142206     0.18   0.859    -.9165751    1.099133
       y1993 |   .8332603   .4330376     1.92   0.054    -.0154778    1.681998
       y1994 |   1.056993   .4673179     2.26   0.024     .1410666    1.972919
       y1995 |   .9294403   .4603641     2.02   0.043     .0271431    1.831737
       y1996 |   .0918078    .585817     0.16   0.875    -1.056372    1.239988
       y1997 |  -1.354144   .6159586    -2.20   0.028    -2.561401   -.1468878
       y1998 |   .0131942   .5867677     0.02   0.982    -1.136849    1.163238
       y1999 |    .223954   .5822686     0.38   0.701    -.9172714    1.365179
       y2000 |   .1201449     .57327     0.21   0.834    -1.003444    1.243734
       y2001 |  -.2574938   .7979129    -0.32   0.747    -1.821374    1.306387
       y2002 |     -.9873   .7045622    -1.40   0.161    -2.368217    .3936166
       y2003 |  -.0297144   .5741804    -0.05   0.959    -1.155087    1.095659
       y2004 |   .2476338   .6859827     0.36   0.718    -1.096868    1.592135
       y2005 |   .6456109   .5659392     1.14   0.254    -.4636096    1.754831
       y2006 |  -.5119271   .5491814    -0.93   0.351    -1.588303    .5644488
       y2007 |    .282965    .524182     0.54   0.589    -.7444128    1.310343
       y2008 |   .0008789    .520148     0.00   0.999    -1.018592     1.02035
       y2009 |   .3709309   .5044811     0.74   0.462    -.6178339    1.359696
       y2010 |   .6553516   .6049144     1.08   0.279    -.5302589    1.840962
       y2011 |   .2071377   .3919426     0.53   0.597    -.5610557    .9753311
       y2012 |   .1884114   .4744356     0.40   0.691    -.7414654    1.118288
       y2013 |   .8115631   .4912566     1.65   0.099    -.1512821    1.774408
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -7.592928   1.609041    -4.72   0.000    -10.74659   -4.439267
-------------+----------------------------------------------------------------
    /lnalpha |   .5095004   .2730985                     -.0257627    1.044764
-------------+----------------------------------------------------------------
       alpha |   1.664459   .4545613                      .9745663    2.842726
------------------------------------------------------------------------------

. 
. *store estimates        
. est sto gmf3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        gmf3 |      1,066 -626.8726  -485.6858      33    1037.372   1201.437
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1037.372
. 
. *Model 4: Demo (NBREG)
.   nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 
> info pop_ln gmf  ///
>   y199* y2* if durable2==2, cluster(ccode)  nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,919
                                                Wald chi2(31)     =     649.91
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -640.70947               Pseudo R2         =     0.3097

                                (Std. Err. adjusted for 106 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   .0839683   .0858714     0.98   0.328    -.0843365    .2522731
     polity2 |   -.309005   .1316742    -2.35   0.019    -.5670817   -.0509284
  public_cor |   .6886923   .6984319     0.99   0.324     -.680209    2.057594
 physical_vd |  -6.222919   .8412001    -7.40   0.000    -7.871641   -4.574198
  express_vd |   4.828766   1.378216     3.50   0.000     2.127513     7.53002
  intensity2 |   .6445795   .1399494     4.61   0.000     .3702838    .9188753
        info |   .0245279   .0062045     3.95   0.000     .0123672    .0366886
      pop_ln |   .5221588   .0868996     6.01   0.000     .3518387    .6924789
         gmf |   .1693435    .179943     0.94   0.347    -.1833384    .5220254
       y1992 |   .2418952   .6281176     0.39   0.700    -.9891928    1.472983
       y1993 |   .5415891   .5695172     0.95   0.342     -.574644    1.657822
       y1994 |  -.4167822   .5645066    -0.74   0.460    -1.523195    .6896305
       y1995 |  -.0429801   .5208191    -0.08   0.934    -1.063767    .9778066
       y1996 |  -.2906785   .6135908    -0.47   0.636    -1.493294    .9119374
       y1997 |   .2683433   .6134708     0.44   0.662    -.9340374    1.470724
       y1998 |  -.2530039   .5004047    -0.51   0.613    -1.233779    .7277713
       y1999 |  -1.023885    .646053    -1.58   0.113    -2.290125    .2423561
       y2000 |  -.2800219     .50939    -0.55   0.583    -1.278408    .7183642
       y2001 |   .4376638   .4885234     0.90   0.370    -.5198245    1.395152
       y2002 |  -.3280165   .4052605    -0.81   0.418    -1.122312    .4662794
       y2003 |  -.0035519   .4554067    -0.01   0.994    -.8961326    .8890287
       y2004 |    .311352   .4776578     0.65   0.515    -.6248401    1.247544
       y2005 |  -.3658013   .5034497    -0.73   0.467    -1.352545     .620942
       y2006 |  -.4762156   .5205056    -0.91   0.360    -1.496388    .5439566
       y2007 |   .0753645   .4170828     0.18   0.857    -.7421027    .8928317
       y2008 |  -.0885588   .5213755    -0.17   0.865    -1.110436    .9333183
       y2009 |   .4121054   .5937876     0.69   0.488     -.751697    1.575908
       y2010 |  -.0707936   .5318052    -0.13   0.894    -1.113113    .9715254
       y2011 |  -.2147376   .4271825    -0.50   0.615       -1.052    .6225247
       y2012 |  -.1582791   .3545268    -0.45   0.655    -.8531388    .5365807
       y2013 |  -.4896439   .4802174    -1.02   0.308    -1.430853     .451565
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -10.19679   2.392144    -4.26   0.000     -14.8853   -5.508271
-------------+----------------------------------------------------------------
    /lnalpha |  -.2446234    .271555                     -.7768614    .2876146
-------------+----------------------------------------------------------------
       alpha |   .7829994   .2126274                       .459847    1.333243
------------------------------------------------------------------------------

. 
. *store estimates        
. est sto gmf4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        gmf4 |      1,919 -928.2025  -640.7095      33    1347.419   1530.884
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1347.419
. 
. ****Create TABLE 17 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab gmf1 gmf2 gmf3 gmf4 using apx_gmf.tex, replace se aic obslast r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" gmf "Global Media Freedom" publi
> c_cor ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" polity2 "Poli
> ty" ///
>  info "Information Flows" pop_ln "Population (ln)" fix "CPJ Unconfirmed" ///
>  gdp_ln "GDP (ln)" gdppc_ln "GDP p/c (ln)" gdppc_cng "$\Delta$ GDP p/c" ) ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(note: file apx_gmf.tex not found)
(output written to apx_gmf.tex)

. 
. *Table 18: w/ Freedom House's Press Freedom in the World covariate
. 
. *Model 1: Full (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln fhfp ///
>   y199* y2*, cluster(ccode)  nolog 
note: y1992 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      3,429
                                                Wald chi2(30)     =     571.18
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -1310.4993               Pseudo R2         =     0.2384

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.2380448   .0777812    -3.06   0.002    -.3904931   -.0855964
     polity2 |   .0398186   .0269525     1.48   0.140    -.0130074    .0926446
  public_cor |   .4082606   .5625084     0.73   0.468    -.6942356    1.510757
 physical_vd |  -4.419655   .7308578    -6.05   0.000     -5.85211     -2.9872
  express_vd |    4.32396   .7550255     5.73   0.000     2.844137    5.803782
  intensity2 |   1.201336   .1378435     8.72   0.000     .9311676    1.471504
        info |   .0324181   .0059943     5.41   0.000     .0206694    .0441668
      pop_ln |   .3971383   .0741449     5.36   0.000     .2518169    .5424597
        fhfp |   .0292136   .0087222     3.35   0.001     .0121184    .0463087
       y1992 |          0  (omitted)
       y1993 |   .7691908    .376599     2.04   0.041     .0310703    1.507311
       y1994 |   .4425182   .3712711     1.19   0.233    -.2851597    1.170196
       y1995 |   .2894282   .3441064     0.84   0.400    -.3850079    .9638643
       y1996 |  -.3628771   .3955347    -0.92   0.359    -1.138111    .4123567
       y1997 |  -.1212533   .4041543    -0.30   0.764    -.9133812    .6708746
       y1998 |  -.1194678   .3489793    -0.34   0.732    -.8034547    .5645192
       y1999 |  -.2569919   .4910711    -0.52   0.601    -1.219473    .7054897
       y2000 |  -.2926674   .3821994    -0.77   0.444    -1.041764    .4564297
       y2001 |   .2117722   .3940471     0.54   0.591    -.5605459    .9840902
       y2002 |  -.6056492   .3583956    -1.69   0.091    -1.308092    .0967933
       y2003 |  -.0829816   .3148478    -0.26   0.792     -.700072    .5341088
       y2004 |   .2222374   .3496907     0.64   0.525    -.4631438    .9076187
       y2005 |  -.1536058    .357919    -0.43   0.668    -.8551141    .5479025
       y2006 |  -.7085472   .3814181    -1.86   0.063    -1.456113    .0390186
       y2007 |  -.1171455   .3104449    -0.38   0.706    -.7256062    .4913153
       y2008 |  -.5312859    .380084    -1.40   0.162    -1.276237    .2136652
       y2009 |    .008648   .3903622     0.02   0.982    -.7564478    .7737439
       y2010 |  -.0411581    .392737    -0.10   0.917    -.8109086    .7285923
       y2011 |  -.1397832   .2990626    -0.47   0.640    -.7259352    .4463687
       y2012 |   .1959231    .235577     0.83   0.406    -.2657992    .6576455
       y2013 |   .1435108   .2888778     0.50   0.619    -.4226793    .7097008
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -12.38374    1.73055    -7.16   0.000    -15.77555   -8.991922
-------------+----------------------------------------------------------------
    /lnalpha |    .654937    .234373                      .1955744      1.1143
-------------+----------------------------------------------------------------
       alpha |   1.925021   .4511729                      1.216009    3.047433
------------------------------------------------------------------------------

. 
. *store estimates        
. est sto fh1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
         fh1 |      3,429 -1720.698  -1310.499      32    2684.999   2881.479
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=2684.999
. 
. *Model 2: Auto (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln fhfp  ///
>    y199* y2* if durable2==0, cluster(ccode) nolog
note: y1992 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =        560
                                                Wald chi2(30)     =   13312.35
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -158.52921               Pseudo R2         =     0.2672

                                 (Std. Err. adjusted for 49 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4274107   .2525867    -1.69   0.091    -.9224715      .06765
     polity2 |  -.1141948   .2264997    -0.50   0.614    -.5581261    .3297365
  public_cor |  -.7446122    1.46111    -0.51   0.610    -3.608336    2.119112
 physical_vd |  -3.191278   1.637223    -1.95   0.051    -6.400177    .0176208
  express_vd |   3.239211   2.114813     1.53   0.126    -.9057458    7.384168
  intensity2 |   1.943636   .3441829     5.65   0.000      1.26905    2.618222
        info |   .0360429    .012155     2.97   0.003     .0122194    .0598663
      pop_ln |    .225463   .1564349     1.44   0.150    -.0811437    .5320698
        fhfp |   .0301614   .0202179     1.49   0.136    -.0094649    .0697877
       y1992 |          0  (omitted)
       y1993 |   .4743059   .9090906     0.52   0.602    -1.307479    2.256091
       y1994 |   .1463478   .7315154     0.20   0.841    -1.287396    1.580092
       y1995 |  -.7145579   1.101302    -0.65   0.516     -2.87307    1.443954
       y1996 |  -1.076442   1.405737    -0.77   0.444    -3.831637    1.678752
       y1997 |  -.2701135   .9177747    -0.29   0.769    -2.068919    1.528692
       y1998 |  -1.025245   .9710042    -1.06   0.291    -2.928379    .8778878
       y1999 |   .3412689   1.013273     0.34   0.736    -1.644709    2.327247
       y2000 |  -1.207367   1.417867    -0.85   0.394    -3.986335    1.571601
       y2001 |  -.5237467   1.645741    -0.32   0.750     -3.74934    2.701846
       y2002 |  -2.333907     .88325    -2.64   0.008    -4.065046    -.602769
       y2003 |  -.2956797   1.100022    -0.27   0.788    -2.451682    1.860323
       y2004 |  -.6024616   1.345504    -0.45   0.654      -3.2396    2.034677
       y2005 |  -.2185049   1.128082    -0.19   0.846    -2.429505    1.992495
       y2006 |   .2694303   1.083898     0.25   0.804     -1.85497     2.39383
       y2007 |   .6057605   1.381438     0.44   0.661    -2.101808    3.313329
       y2008 |   -17.9855    .547456   -32.85   0.000    -19.05849   -16.91251
       y2009 |    .496079   1.067847     0.46   0.642    -1.596862     2.58902
       y2010 |  -.6811438   1.591029    -0.43   0.669    -3.799503    2.437215
       y2011 |   .4135346   .7678061     0.54   0.590    -1.091338    1.918407
       y2012 |   .6252302   .2440044     2.56   0.010     .1469904     1.10347
       y2013 |   .4433526   .0712769     6.22   0.000     .3036525    .5830527
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |   -10.0948   3.265633    -3.09   0.002    -16.49533   -3.694279
-------------+----------------------------------------------------------------
    /lnalpha |   1.115207   .5537697                      .0298379    2.200575
-------------+----------------------------------------------------------------
       alpha |   3.050198   1.689107                      1.030288    9.030206
------------------------------------------------------------------------------

. 
. *store estimates        
. est sto fh2

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
         fh2 |        560 -216.3233  -158.5292      32    381.0584   519.5524
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=381.058
. 
. *Model 3: Ano (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln  fhfp   ///
>   y199* y2* if durable2==1, cluster(ccode)  nolog
note: y1992 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,020
                                                Wald chi2(30)     =     514.78
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -450.88556               Pseudo R2         =     0.2397

                                 (Std. Err. adjusted for 84 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.5553078   .1386783    -4.00   0.000    -.8271123   -.2835033
     polity2 |   .0418162   .0395328     1.06   0.290    -.0356668    .1192991
  public_cor |   .1932335   .6912623     0.28   0.780    -1.161616    1.548083
 physical_vd |  -4.038294   .9746062    -4.14   0.000    -5.948487   -2.128101
  express_vd |   4.490141   .9560436     4.70   0.000      2.61633    6.363952
  intensity2 |   1.061332   .1474486     7.20   0.000     .7723376    1.350326
        info |   .0309647   .0077919     3.97   0.000     .0156928    .0462365
      pop_ln |   .3111415   .1071003     2.91   0.004     .1012288    .5210542
        fhfp |   .0280484    .009193     3.05   0.002     .0100304    .0460665
       y1992 |          0  (omitted)
       y1993 |   .8192547   .4605099     1.78   0.075    -.0833281    1.721838
       y1994 |   1.058979   .4704364     2.25   0.024     .1369406    1.981017
       y1995 |   .8198584   .4135226     1.98   0.047     .0093691    1.630348
       y1996 |  -.0309839   .5296396    -0.06   0.953    -1.069058    1.007091
       y1997 |  -1.475489   .6038887    -2.44   0.015    -2.659089   -.2918885
       y1998 |   .0284666    .548992     0.05   0.959    -1.047538    1.104471
       y1999 |   .1205617   .5514112     0.22   0.827    -.9601843    1.201308
       y2000 |  -.0005708   .5296131    -0.00   0.999    -1.038593    1.037452
       y2001 |  -.3378394   .7820896    -0.43   0.666    -1.870707    1.195028
       y2002 |  -.9372986   .6914851    -1.36   0.175    -2.292585    .4179874
       y2003 |   -.141402   .5133451    -0.28   0.783     -1.14754     .864736
       y2004 |    .144101   .6717944     0.21   0.830    -1.172592    1.460794
       y2005 |   .5073389   .5239297     0.97   0.333    -.5195446    1.534222
       y2006 |  -.5837367   .5017398    -1.16   0.245    -1.567129    .3996551
       y2007 |    .171349   .4799128     0.36   0.721    -.7692627    1.111961
       y2008 |  -.0691567   .5142751    -0.13   0.893    -1.077117     .938804
       y2009 |   .2532765   .4533382     0.56   0.576    -.6352501    1.141803
       y2010 |   .5087979   .5731672     0.89   0.375    -.6145893    1.632185
       y2011 |   .2725614   .3892434     0.70   0.484    -.4903416    1.035464
       y2012 |   .0601665   .4336545     0.14   0.890    -.7897807    .9101137
       y2013 |   .7753929   .4671616     1.66   0.097     -.140227    1.691013
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -10.62084   1.642096    -6.47   0.000    -13.83929   -7.402388
-------------+----------------------------------------------------------------
    /lnalpha |   .2876158   .3281643                     -.3555743     .930806
-------------+----------------------------------------------------------------
       alpha |   1.333245   .4375234                      .7007709    2.536553
------------------------------------------------------------------------------

. 
. *store estimates        
. est sto fh3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
         fh3 |      1,020 -593.0153  -450.8856      32    965.7711   1123.453
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=965.771
. 
. *Model 4: Demo (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln fhfp   ///
>   y199* y2* if durable2==2, cluster(ccode)  nolog
note: y1992 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,849
                                                Wald chi2(30)     =     566.33
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -621.59996               Pseudo R2         =     0.3073

                                (Std. Err. adjusted for 106 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |    .071537   .0854961     0.84   0.403    -.0960323    .2391064
     polity2 |  -.3218383   .1604232    -2.01   0.045    -.6362621   -.0074146
  public_cor |   .6430389   .7126158     0.90   0.367    -.7536624     2.03974
 physical_vd |  -6.113206   .9251317    -6.61   0.000    -7.926431   -4.299981
  express_vd |   4.764332   1.325518     3.59   0.000     2.166365    7.362298
  intensity2 |   .6026269   .1498138     4.02   0.000     .3089973    .8962566
        info |   .0234583   .0061387     3.82   0.000     .0114267      .03549
      pop_ln |   .5290333   .0939511     5.63   0.000     .3448925    .7131741
        fhfp |   .0066373   .0154978     0.43   0.668    -.0237379    .0370124
       y1992 |          0  (omitted)
       y1993 |   .5459211   .5585012     0.98   0.328    -.5487211    1.640563
       y1994 |  -.4226174   .5470425    -0.77   0.440    -1.494801    .6495661
       y1995 |  -.0606444   .5343955    -0.11   0.910     -1.10804    .9867514
       y1996 |  -.2937782   .6070984    -0.48   0.628    -1.483669    .8961128
       y1997 |   .2674835   .6267852     0.43   0.670    -.9609929     1.49596
       y1998 |  -.2759831   .5022051    -0.55   0.583    -1.260287    .7083208
       y1999 |  -1.017767   .6378339    -1.60   0.111    -2.267899    .2323641
       y2000 |   -.240432   .5065469    -0.47   0.635    -1.233246    .7523816
       y2001 |   .4622039   .4965078     0.93   0.352    -.5109336    1.435341
       y2002 |  -.3388594   .3697568    -0.92   0.359    -1.063569    .3858505
       y2003 |   .0123296   .4356244     0.03   0.977    -.8414785    .8661377
       y2004 |   .3107292   .4716347     0.66   0.510    -.6136577    1.235116
       y2005 |  -.3720289   .4977309    -0.75   0.455    -1.347564    .6035058
       y2006 |  -.4999422   .5155688    -0.97   0.332    -1.510438    .5105542
       y2007 |   .0367879   .4149017     0.09   0.929    -.7764044    .8499802
       y2008 |  -.1157937   .5125801    -0.23   0.821    -1.120432    .8888448
       y2009 |   .3917029   .5926855     0.66   0.509    -.7699394    1.553345
       y2010 |  -.0922947    .528226    -0.17   0.861    -1.127599    .9430093
       y2011 |  -.2296787   .4260593    -0.54   0.590     -1.06474    .6053822
       y2012 |  -.1685656   .3495655    -0.48   0.630    -.8537014    .5165702
       y2013 |   -.488256   .4752754    -1.03   0.304    -1.419779    .4432667
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -10.03326   2.763884    -3.63   0.000    -15.45038   -4.616151
-------------+----------------------------------------------------------------
    /lnalpha |  -.2555149   .2744483                     -.7934237     .282394
-------------+----------------------------------------------------------------
       alpha |   .7745176   .2125651                      .4522936    1.326301
------------------------------------------------------------------------------

. 
. *store estimates        
. est sto fh4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
         fh4 |      1,849 -897.3173     -621.6      32      1307.2   1483.917
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1307.2
. 
. ****Create TABLE 18 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab fh1 fh2 fh3 fh4 using apx_fh.tex, replace se aic obslast r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" fhfp "Press Freedom, Freedom Hou
> se" public_cor ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" polity2 "Poli
> ty" ///
>  info "Information Flows" pop_ln "Population (ln)" fix "CPJ Unconfirmed" ///
>  gdp_ln "GDP (ln)" gdppc_ln "GDP p/c (ln)" gdppc_cng "$\Delta$ GDP p/c" ) ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(note: file apx_fh.tex not found)
(output written to apx_fh.tex)

. 
. *Table 19: w/ GDP covariate
. 
. *Model 1: Full (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln  gdp_ln ///
>   y199* y2* , cluster(ccode)  nolog 
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      3,502
                                                Wald chi2(31)     =     741.53
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -1288.6257               Pseudo R2         =     0.2305

                                (Std. Err. adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   -.388359   .0852684    -4.55   0.000    -.5554821   -.2212359
     polity2 |     .01897   .0237098     0.80   0.424    -.0275004    .0654404
  public_cor |    .577084   .5684219     1.02   0.310    -.5370025    1.691171
 physical_vd |  -4.452678   .7484366    -5.95   0.000    -5.919586   -2.985769
  express_vd |   3.337566   .7656077     4.36   0.000     1.837003     4.83813
  intensity2 |    1.23442   .1245466     9.91   0.000     .9903129    1.478527
        info |    .016431   .0089193     1.84   0.065    -.0010505    .0339126
      pop_ln |   .2292625   .1859176     1.23   0.218    -.1351294    .5936544
      gdp_ln |   .1843823   .1667338     1.11   0.269    -.1424098    .5111745
       y1992 |   .0264467   .4761447     0.06   0.956    -.9067796    .9596731
       y1993 |   .5499308   .4596131     1.20   0.231    -.3508944    1.450756
       y1994 |   .8650532   .4581611     1.89   0.059    -.0329261    1.763033
       y1995 |   .3453284   .3952786     0.87   0.382    -.4294034     1.12006
       y1996 |  -.2091398    .445887    -0.47   0.639    -1.083062    .6647826
       y1997 |  -.0705534   .4550138    -0.16   0.877    -.9623642    .8212573
       y1998 |  -.0329396   .3928421    -0.08   0.933     -.802896    .7370169
       y1999 |  -.0785868   .5148792    -0.15   0.879    -1.087731    .9305579
       y2000 |  -.1129815   .4402784    -0.26   0.797    -.9759113    .7499483
       y2001 |   .3908519   .4379448     0.89   0.372    -.4675042    1.249208
       y2002 |  -.3915155   .4337735    -0.90   0.367    -1.241696    .4586649
       y2003 |   .1195244   .3985687     0.30   0.764    -.6616558    .9007046
       y2004 |   .4373375   .3910345     1.12   0.263    -.3290761    1.203751
       y2005 |  -.0119855   .3957185    -0.03   0.976    -.7875794    .7636084
       y2006 |  -.4668016   .4350755    -1.07   0.283    -1.319534    .3859308
       y2007 |   .0671901   .3585731     0.19   0.851    -.6356003    .7699804
       y2008 |  -.3086154   .4233744    -0.73   0.466    -1.138414    .5211831
       y2009 |   .2361754   .4316038     0.55   0.584    -.6097526    1.082103
       y2010 |   .2779229   .4335194     0.64   0.521    -.5717596    1.127605
       y2011 |   .0204414   .3415103     0.06   0.952    -.6489064    .6897893
       y2012 |   .0392453   .3127623     0.13   0.900    -.5737574    .6522481
       y2013 |   .0405683   .3976299     0.10   0.919     -.738772    .8199086
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -10.77103   1.533566    -7.02   0.000    -13.77676   -7.765295
-------------+----------------------------------------------------------------
    /lnalpha |   .6500663   .2387965                      .1820338    1.118099
-------------+----------------------------------------------------------------
       alpha |   1.915668   .4574547                      1.199655    3.059033
------------------------------------------------------------------------------

. 
. *store estimates        
. est sto gdp1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        gdp1 |      3,502 -1674.559  -1288.626      33    2643.251   2846.567
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=2643.251
. 
. *Model 2: Auto (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo  pop_ln gdp_ln ///
>     y199* y2*   if durable2==0, cluster(ccode)
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -449.97382  
Iteration 1:   log pseudolikelihood = -402.23495  (backed up)
Iteration 2:   log pseudolikelihood = -272.32368  
Iteration 3:   log pseudolikelihood = -178.77166  
Iteration 4:   log pseudolikelihood = -152.61349  
Iteration 5:   log pseudolikelihood = -149.12784  
Iteration 6:   log pseudolikelihood =  -149.1111  
Iteration 7:   log pseudolikelihood = -149.11086  
Iteration 8:   log pseudolikelihood = -149.11084  
Iteration 9:   log pseudolikelihood = -149.11084  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -273.57295  (not concave)
Iteration 1:   log pseudolikelihood = -200.15022  
Iteration 2:   log pseudolikelihood = -191.97279  
Iteration 3:   log pseudolikelihood = -191.96741  
Iteration 4:   log pseudolikelihood = -191.96741  

Fitting full model:

Iteration 0:   log pseudolikelihood = -191.96741  (not concave)
Iteration 1:   log pseudolikelihood = -166.67698  
Iteration 2:   log pseudolikelihood = -151.51551  (not concave)
Iteration 3:   log pseudolikelihood = -140.56342  
Iteration 4:   log pseudolikelihood = -138.95955  
Iteration 5:   log pseudolikelihood = -138.80453  
Iteration 6:   log pseudolikelihood = -138.79155  
Iteration 7:   log pseudolikelihood = -138.78867  
Iteration 8:   log pseudolikelihood = -138.78804  
Iteration 9:   log pseudolikelihood = -138.78793  
Iteration 10:  log pseudolikelihood = -138.78792  

Negative binomial regression                    Number of obs     =        563
                                                Wald chi2(28)     =          .
Dispersion           = mean                     Prob > chi2       =          .
Log pseudolikelihood = -138.78792               Pseudo R2         =     0.2770

                                 (Std. Err. adjusted for 50 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.5494635    .184353    -2.98   0.003    -.9107888   -.1881382
     polity2 |   .2454385   .2475578     0.99   0.321    -.2397659    .7306429
  public_cor |  -1.581861   1.089712    -1.45   0.147    -3.717657    .5539342
 physical_vd |  -2.143706   1.726691    -1.24   0.214    -5.527958    1.240546
  express_vd |   .7697076   1.577541     0.49   0.626    -2.322215     3.86163
  intensity2 |   1.842522   .2920048     6.31   0.000     1.270203     2.41484
        info |   .0426313   .0168858     2.52   0.012     .0095357    .0757269
      pop_ln |   .2065614   .2515871     0.82   0.412    -.2865403    .6996631
      gdp_ln |   .0500126   .3107109     0.16   0.872    -.5589695    .6589948
       y1992 |    13.9068   .7573736    18.36   0.000     12.42238    15.39123
       y1993 |   14.30594   .8821638    16.22   0.000     12.57693    16.03495
       y1994 |   14.80775   .7968741    18.58   0.000     13.24591     16.3696
       y1995 |   13.02521   .8729521    14.92   0.000     11.31425    14.73616
       y1996 |   12.97085   1.161584    11.17   0.000     10.69419    15.24751
       y1997 |   13.51218   .8858647    15.25   0.000     11.77592    15.24844
       y1998 |   12.46062   .8459011    14.73   0.000     10.80268    14.11856
       y1999 |   13.06629   .7500986    17.42   0.000     11.59612    14.53645
       y2000 |   12.63456   1.069366    11.82   0.000     10.53864    14.73048
       y2001 |   12.97583   1.458429     8.90   0.000     10.11737     15.8343
       y2002 |    11.0991    .832841    13.33   0.000     9.466764    12.73144
       y2003 |   12.99278   1.020252    12.73   0.000     10.99312    14.99244
       y2004 |    12.7652   1.298015     9.83   0.000     10.22113    15.30926
       y2005 |   13.18952   1.073552    12.29   0.000     11.08539    15.29364
       y2006 |   13.74845   1.088754    12.63   0.000     11.61453    15.88237
       y2007 |   14.07613    1.17654    11.96   0.000     11.77016    16.38211
       y2008 |  -6.557768   .4762885   -13.77   0.000    -7.491276    -5.62426
       y2009 |   13.73347   .9607182    14.29   0.000     11.85049    15.61644
       y2010 |   12.64685   1.449923     8.72   0.000     9.805055    15.48865
       y2011 |   13.18131   1.680279     7.84   0.000     9.888025     16.4746
       y2012 |   12.91776   1.103365    11.71   0.000     10.75521    15.08032
       y2013 |   -6.14408   .2445112   -25.13   0.000    -6.623313   -5.664847
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -18.52311   3.460005    -5.35   0.000     -25.3046   -11.74163
-------------+----------------------------------------------------------------
    /lnalpha |   .8369369   .7560217                     -.6448384    2.318712
-------------+----------------------------------------------------------------
       alpha |   2.309283   1.745868                      .5247474    10.16258
------------------------------------------------------------------------------

. 
. *store estimates        
. est sto gdp2

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        gdp2 |        563 -191.9674  -138.7879      30    337.5758   467.5742
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=339.576 (but changes, for some reason)
. 
. *Model 3: Ano (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo  pop_ln gdp_ln  ///
>   y199* y2* if durable2==1, cluster(ccode)  nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,031
                                                Wald chi2(31)     =     514.23
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -422.43009               Pseudo R2         =     0.2337

                                 (Std. Err. adjusted for 84 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.6362178   .1367082    -4.65   0.000     -.904161   -.3682746
     polity2 |   .0305979   .0368487     0.83   0.406    -.0416242      .10282
  public_cor |  -.1314405   .6726359    -0.20   0.845    -1.449783    1.186902
 physical_vd |   -3.57902   .9475486    -3.78   0.000    -5.436181   -1.721859
  express_vd |    3.42082   .9926921     3.45   0.001     1.475179    5.366461
  intensity2 |   1.089343   .1556152     7.00   0.000      .784343    1.394343
        info |   .0283286   .0102248     2.77   0.006     .0082883    .0483688
      pop_ln |   .3423357   .2377311     1.44   0.150    -.1236087      .80828
      gdp_ln |    .028024   .1828397     0.15   0.878    -.3303354    .3863833
       y1992 |  -.3768247   .5454248    -0.69   0.490    -1.445838    .6921883
       y1993 |   .4473718   .5463257     0.82   0.413     -.623407    1.518151
       y1994 |   1.010131   .5563543     1.82   0.069    -.0803038    2.100565
       y1995 |   .8977347   .4799124     1.87   0.061    -.0428762    1.838346
       y1996 |   .1245123   .5911704     0.21   0.833     -1.03416    1.283185
       y1997 |  -1.313745   .6306823    -2.08   0.037    -2.549859   -.0776301
       y1998 |   .0245621   .5713004     0.04   0.966    -1.095166     1.14429
       y1999 |    .369461   .5499061     0.67   0.502    -.7083352    1.447257
       y2000 |    .009573   .6253458     0.02   0.988    -1.216082    1.235228
       y2001 |  -.1885403   .7693773    -0.25   0.806    -1.696492    1.319412
       y2002 |  -.9505901   .6703629    -1.42   0.156    -2.264477    .3632971
       y2003 |  -.3242239    .618546    -0.52   0.600    -1.536552     .888104
       y2004 |   .2220332    .669511     0.33   0.740    -1.090184    1.534251
       y2005 |    .209262   .5971951     0.35   0.726    -.9612189    1.379743
       y2006 |  -.7894175   .5972195    -1.32   0.186    -1.959946    .3811111
       y2007 |   -.307603   .6287332    -0.49   0.625    -1.539897    .9246914
       y2008 |  -.1626392   .5795089    -0.28   0.779    -1.298456    .9731773
       y2009 |  -.1831301   .5304422    -0.35   0.730    -1.222778    .8565175
       y2010 |   .5309084   .6523119     0.81   0.416    -.7475995    1.809416
       y2011 |   .2376634    .414814     0.57   0.567    -.5753572    1.050684
       y2012 |   .0063486   .5668146     0.01   0.991    -1.104588    1.117285
       y2013 |   .7485321      .4772     1.57   0.117    -.1867626    1.683827
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.101935   1.518679    -5.99   0.000    -12.07849    -6.12538
-------------+----------------------------------------------------------------
    /lnalpha |   .3570805   .3471546                     -.3233301    1.037491
-------------+----------------------------------------------------------------
       alpha |   1.429151   .4961363                      .7237349    2.822127
------------------------------------------------------------------------------

. 
. *store estimates
. est sto gdp3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        gdp3 |      1,031 -551.2701  -422.4301      33    910.8602   1073.824
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=910.8602
. 
. *Model 4: Demo (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln gdp_ln ///
>   y199* y2* if durable2==2, cluster(ccode)  nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,908
                                                Wald chi2(31)     =     921.99
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -631.96775               Pseudo R2         =     0.3162

                                (Std. Err. adjusted for 106 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   .0516042   .0727937     0.71   0.478    -.0910689    .1942773
     polity2 |   -.302343   .1036462    -2.92   0.004    -.5054858   -.0992001
  public_cor |   1.600515   .6403388     2.50   0.012     .3454741    2.855556
 physical_vd |  -6.313099   .9195093    -6.87   0.000    -8.115304   -4.510894
  express_vd |   4.299648   1.268618     3.39   0.001     1.813201    6.786094
  intensity2 |    .700147   .1438899     4.87   0.000      .418128     .982166
        info |   .0054174    .009531     0.57   0.570     -.013263    .0240978
      pop_ln |   .1143369   .1953366     0.59   0.558    -.2685158    .4971897
      gdp_ln |   .4069896   .1607794     2.53   0.011     .0918677    .7221114
       y1992 |   .2115433    .620751     0.34   0.733    -1.005106    1.428193
       y1993 |   .4838498   .5756416     0.84   0.401    -.6443871    1.612087
       y1994 |  -.3853839   .5308701    -0.73   0.468     -1.42587    .6551024
       y1995 |   .0137108   .4962479     0.03   0.978    -.9589172    .9863388
       y1996 |  -.2094262   .6060689    -0.35   0.730      -1.3973     .978447
       y1997 |   .4562961   .6141983     0.74   0.458    -.7475105    1.660103
       y1998 |  -.0548386   .4956245    -0.11   0.912    -1.026245    .9165676
       y1999 |  -.7510072   .6370078    -1.18   0.238     -1.99952    .4975051
       y2000 |   .0057486    .510112     0.01   0.991    -.9940527     1.00555
       y2001 |   .7041897   .4866721     1.45   0.148    -.2496701     1.65805
       y2002 |  -.0544206    .441234    -0.12   0.902    -.9192234    .8103822
       y2003 |   .2774012     .49053     0.57   0.572    -.6840199    1.238822
       y2004 |   .5145667   .4844866     1.06   0.288    -.4350095    1.464143
       y2005 |   -.219822    .508469    -0.43   0.666    -1.216403    .7767589
       y2006 |  -.3619999   .5357324    -0.68   0.499    -1.412016    .6880163
       y2007 |   .1845757   .4247137     0.43   0.664    -.6478479    1.016999
       y2008 |   .0255812   .5283757     0.05   0.961    -1.010016    1.061179
       y2009 |   .4797099   .6086806     0.79   0.431    -.7132822    1.672702
       y2010 |   .0110942   .5371202     0.02   0.984    -1.041642     1.06383
       y2011 |  -.1450755   .4328116    -0.34   0.737    -.9933706    .7032197
       y2012 |  -.1084795   .3596225    -0.30   0.763    -.8133266    .5963676
       y2013 |    -.40615   .4615184    -0.88   0.379     -1.31071    .4984095
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -12.08557   1.970921    -6.13   0.000     -15.9485   -8.222635
-------------+----------------------------------------------------------------
    /lnalpha |  -.3104595    .267893                     -.8355202    .2146012
-------------+----------------------------------------------------------------
       alpha |     .73311   .1963951                      .4336488    1.239368
------------------------------------------------------------------------------

. 
. *store estimates
. est sto gdp4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        gdp4 |      1,908  -924.223  -631.9677      33    1329.935   1513.211
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1329.935
. 
. ****Create TABLE 19 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab gdp1 gdp2 gdp3 gdp4 using apx_gdp.tex, replace se aic obslast r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" fix "CPJ Unconfirmed" ///
>  gdp_ln "GDP (ln)" gdppc_ln "GDP p/c (ln)" gdppc_cng "$\Delta$ GDP p/c" ) ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(note: file apx_gdp.tex not found)
(output written to apx_gdp.tex)

. 
. *Table 20: w/ GDP per capita covariate
. 
. *Model 1: Full (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln gdppc_ln gdppc_cng ///
>   y199* y2* , cluster(ccode)  nolog 
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      3,468
                                                Wald chi2(32)     =     720.21
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -1251.7363               Pseudo R2         =     0.2424

                                (Std. Err. adjusted for 158 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3816441   .0877092    -4.35   0.000     -.553551   -.2097372
     polity2 |   .0216617   .0250884     0.86   0.388    -.0275106     .070834
  public_cor |   .7168616   .5171002     1.39   0.166    -.2966362    1.730359
 physical_vd |  -4.358948   .7263368    -6.00   0.000    -5.782541   -2.935354
  express_vd |   3.404284   .7460122     4.56   0.000     1.942127    4.866441
  intensity2 |    1.13059   .1169661     9.67   0.000      .901341     1.35984
        info |   .0106417   .0092367     1.15   0.249    -.0074619    .0287454
      pop_ln |   .4742347   .0698407     6.79   0.000     .3373495    .6111199
    gdppc_ln |   .2817913   .1603453     1.76   0.079    -.0324798    .5960624
   gdppc_cng |  -.0567698   .0136881    -4.15   0.000     -.083598   -.0299417
       y1992 |  -.2992736   .4674722    -0.64   0.522    -1.215502     .616955
       y1993 |   .2979911   .4754179     0.63   0.531    -.6338107    1.229793
       y1994 |   .3315563    .451642     0.73   0.463    -.5536457    1.216758
       y1995 |   .3537972   .4147196     0.85   0.394    -.4590384    1.166633
       y1996 |  -.1186189   .4348146    -0.27   0.785    -.9708399    .7336021
       y1997 |   .0165175    .445515     0.04   0.970    -.8566758    .8897109
       y1998 |  -.0150496   .3918591    -0.04   0.969    -.7830794    .7529801
       y1999 |  -.0592597   .4834892    -0.12   0.902    -1.006881    .8883617
       y2000 |   .0537896   .4267372     0.13   0.900       -.7826    .8901793
       y2001 |   .4658081   .4399123     1.06   0.290    -.3964042     1.32802
       y2002 |  -.2937835   .4036523    -0.73   0.467    -1.084927    .4973605
       y2003 |   .2548831   .3887095     0.66   0.512    -.5069735     1.01674
       y2004 |    .634604   .3804703     1.67   0.095     -.111104    1.380312
       y2005 |   .2099938    .392479     0.54   0.593    -.5592509    .9792386
       y2006 |  -.2148646   .4280858    -0.50   0.616    -1.053897    .6241682
       y2007 |   .2707529   .3493135     0.78   0.438     -.413889    .9553949
       y2008 |  -.2039053   .4132194    -0.49   0.622      -1.0138    .6059898
       y2009 |   .1512544   .4399094     0.34   0.731    -.7109521    1.013461
       y2010 |   .3877862   .4207078     0.92   0.357    -.4367859    1.212358
       y2011 |  -.0310963   .3527816    -0.09   0.930    -.7225356     .660343
       y2012 |   .0451966   .3073232     0.15   0.883    -.5571457    .6475389
       y2013 |    -.00766   .4143119    -0.02   0.985    -.8196965    .8043764
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -12.36296   1.477147    -8.37   0.000    -15.25811     -9.4678
-------------+----------------------------------------------------------------
    /lnalpha |   .4938399   .2400231                      .0234033    .9642766
-------------+----------------------------------------------------------------
       alpha |   1.638596    .393301                      1.023679     2.62289
------------------------------------------------------------------------------

. 
. *store estimates
. est sto gdppc1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
      gdppc1 |      3,468 -1652.148  -1251.736      34    2571.473   2780.618
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=2571.473
. 
. *Model 2: Auto (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo  gdppc_ln gdppc_cng ///
>     y199* y2*   if durable2==0, cluster(ccode)
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -626.18952  
Iteration 1:   log pseudolikelihood = -560.62188  (backed up)
Iteration 2:   log pseudolikelihood = -359.72604  (backed up)
Iteration 3:   log pseudolikelihood = -237.29353  
Iteration 4:   log pseudolikelihood = -165.74094  
Iteration 5:   log pseudolikelihood = -137.85865  
Iteration 6:   log pseudolikelihood = -136.93097  
Iteration 7:   log pseudolikelihood = -136.92913  
Iteration 8:   log pseudolikelihood = -136.92912  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -270.20823  (not concave)
Iteration 1:   log pseudolikelihood = -198.23135  
Iteration 2:   log pseudolikelihood = -190.53337  
Iteration 3:   log pseudolikelihood = -190.52946  
Iteration 4:   log pseudolikelihood = -190.52946  

Fitting full model:

Iteration 0:   log pseudolikelihood = -190.52946  (not concave)
Iteration 1:   log pseudolikelihood = -163.89911  
Iteration 2:   log pseudolikelihood = -157.69552  (not concave)
Iteration 3:   log pseudolikelihood = -138.74021  
Iteration 4:   log pseudolikelihood = -136.27244  
Iteration 5:   log pseudolikelihood = -135.22995  
Iteration 6:   log pseudolikelihood = -135.10086  
Iteration 7:   log pseudolikelihood = -135.09176  
Iteration 8:   log pseudolikelihood = -135.08979  
Iteration 9:   log pseudolikelihood = -135.08939  
Iteration 10:  log pseudolikelihood =  -135.0893  
Iteration 11:  log pseudolikelihood = -135.08927  
Iteration 12:  log pseudolikelihood = -135.08927  

Negative binomial regression                    Number of obs     =        542
                                                Wald chi2(26)     =          .
Dispersion           = mean                     Prob > chi2       =          .
Log pseudolikelihood = -135.08927               Pseudo R2         =     0.2910

                                 (Std. Err. adjusted for 49 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4613059    .189496    -2.43   0.015    -.8327113   -.0899005
     polity2 |   .0174131   .2609806     0.07   0.947    -.4940995    .5289257
  public_cor |  -.8607856   1.008047    -0.85   0.393    -2.836522     1.11495
 physical_vd |  -2.489989   1.923942    -1.29   0.196    -6.260847    1.280869
  express_vd |    2.45326   1.358357     1.81   0.071    -.2090719    5.115591
  intensity2 |   1.624931   .2732429     5.95   0.000     1.089385    2.160478
        info |   .0322894   .0155956     2.07   0.038     .0017226    .0628563
    gdppc_ln |   .1888243   .2538851     0.74   0.457    -.3087813      .68643
   gdppc_cng |  -.0580429    .015557    -3.73   0.000     -.088534   -.0275518
       y1992 |   13.80457   1.028533    13.42   0.000     11.78868    15.82046
       y1993 |   14.87562   .9451479    15.74   0.000     13.02316    16.72807
       y1994 |   14.86134   .8883814    16.73   0.000     13.12014    16.60253
       y1995 |   13.45803   .9468701    14.21   0.000      11.6022    15.31386
       y1996 |    13.6163   1.223833    11.13   0.000     11.21763    16.01497
       y1997 |   14.26409   1.246885    11.44   0.000     11.82024    16.70794
       y1998 |   13.28023    .876008    15.16   0.000     11.56328    14.99717
       y1999 |    13.6754   .7430967    18.40   0.000     12.21895    15.13184
       y2000 |   13.56221    1.06904    12.69   0.000     11.46693    15.65749
       y2001 |   13.81849   1.391733     9.93   0.000     11.09074    16.54623
       y2002 |   12.30735   .8493887    14.49   0.000     10.64258    13.97213
       y2003 |   14.10425   .9340334    15.10   0.000     12.27358    15.93493
       y2004 |   13.82363   1.052239    13.14   0.000     11.76128    15.88598
       y2005 |   14.37027   1.115396    12.88   0.000     12.18413     16.5564
       y2006 |   14.66081   1.152516    12.72   0.000     12.40192     16.9197
       y2007 |   14.98108   1.010851    14.82   0.000     12.99985    16.96231
       y2008 |  -4.998521   .3898412   -12.82   0.000    -5.762596   -4.234447
       y2009 |   14.39005   .8735927    16.47   0.000     12.67784    16.10226
       y2010 |   13.46004   1.318447    10.21   0.000     10.87593    16.04415
       y2011 |   13.70901   1.535568     8.93   0.000     10.69935    16.71867
       y2012 |   13.63198   .9320881    14.63   0.000     11.80512    15.45884
       y2013 |  -5.432547   .2723055   -19.95   0.000    -5.966256   -4.898838
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -17.98293    2.50663    -7.17   0.000    -22.89584   -13.07003
-------------+----------------------------------------------------------------
    /lnalpha |   .0682665   1.402493                      -2.68057    2.817103
-------------+----------------------------------------------------------------
       alpha |   1.070651    1.50158                      .0685241    16.72832
------------------------------------------------------------------------------

. 
. *store estimates
. est sto gdppc2

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
      gdppc2 |        542 -190.5295  -135.0893      28    326.1785    446.446
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=330.179 (but changes, for some reason)
. 
. *Model 3: Ano (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln gdppc_ln gdppc_cng   ///
>   y199* y2* if durable2==1, cluster(ccode)  nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,026
                                                Wald chi2(32)     =     642.60
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood =  -405.1106               Pseudo R2         =     0.2392

                                 (Std. Err. adjusted for 82 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   -.558418   .1263778    -4.42   0.000     -.806114    -.310722
     polity2 |    .021623   .0364216     0.59   0.553     -.049762     .093008
  public_cor |  -.5613774   .7243716    -0.77   0.438     -1.98112    .8583649
 physical_vd |  -3.620678   .8997564    -4.02   0.000    -5.384168   -1.857188
  express_vd |   3.691285   1.017165     3.63   0.000     1.697678    5.684893
  intensity2 |   .9484183   .1553339     6.11   0.000     .6439694    1.252867
        info |    .020905   .0094319     2.22   0.027     .0024188    .0393912
      pop_ln |   .4555047   .1148997     3.96   0.000     .2303055     .680704
    gdppc_ln |   .1287269   .1844133     0.70   0.485    -.2327166    .4901704
   gdppc_cng |  -.0336505   .0122058    -2.76   0.006    -.0575735   -.0097276
       y1992 |  -.5368533   .5929675    -0.91   0.365    -1.699048    .6253416
       y1993 |   .1870319   .5952228     0.31   0.753    -.9795833    1.353647
       y1994 |   .6824811   .6263233     1.09   0.276    -.5450899    1.910052
       y1995 |   .8853939   .5160604     1.72   0.086     -.126066    1.896854
       y1996 |   .2431086   .6073253     0.40   0.689    -.9472272    1.433444
       y1997 |  -1.294773   .6187914    -2.09   0.036    -2.507582   -.0819642
       y1998 |   .0960713   .5728054     0.17   0.867    -1.026607    1.218749
       y1999 |   .5508143   .5329031     1.03   0.301    -.4936565    1.595285
       y2000 |   .1582263    .637069     0.25   0.804    -1.090406    1.406859
       y2001 |  -.0475033   .7895242    -0.06   0.952    -1.594942    1.499936
       y2002 |  -.8407008   .6851939    -1.23   0.220    -2.183656    .5022546
       y2003 |  -.2626044   .6267694    -0.42   0.675     -1.49105     .965841
       y2004 |   .4070721   .6430967     0.63   0.527    -.8533742    1.667519
       y2005 |   .3617613   .5987296     0.60   0.546    -.8117271     1.53525
       y2006 |  -.5709335   .6200032    -0.92   0.357    -1.786118    .6442505
       y2007 |  -.1166312   .5974974    -0.20   0.845    -1.287705    1.054442
       y2008 |  -.0320427    .583565    -0.05   0.956    -1.175809    1.111724
       y2009 |  -.0893213   .5242596    -0.17   0.865    -1.116851    .9382087
       y2010 |   .6399241   .6150066     1.04   0.298    -.5654667    1.845315
       y2011 |   .1436866   .4233749     0.34   0.734     -.686113    .9734862
       y2012 |   .0942477   .5478257     0.17   0.863    -.9794709    1.167966
       y2013 |   .7193028   .5307911     1.36   0.175    -.3210287    1.759634
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -10.83424   1.757493    -6.16   0.000    -14.27886   -7.389617
-------------+----------------------------------------------------------------
    /lnalpha |   .1973332   .4652098                     -.7144613    1.109128
-------------+----------------------------------------------------------------
       alpha |    1.21815   .5666952                      .4894557    3.031712
------------------------------------------------------------------------------

. 
. *store estimates
. est sto gdppc3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
      gdppc3 |      1,026 -532.5099  -405.1106      34    878.2212   1045.958
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *878.2212
. 
. *Model 4: Demo (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln gdppc_ln gdppc_cng  ///
>   y199* y2* if durable2==2, cluster(ccode)  nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,900
                                                Wald chi2(32)     =     889.39
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -630.90717               Pseudo R2         =     0.3167

                                (Std. Err. adjusted for 106 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   .0491447   .0723786     0.68   0.497    -.0927147    .1910041
     polity2 |  -.3057035    .104772    -2.92   0.004    -.5110529    -.100354
  public_cor |   1.595967   .6556614     2.43   0.015     .3108939    2.881039
 physical_vd |   -6.12902   .9472168    -6.47   0.000    -7.985531   -4.272509
  express_vd |   4.068624     1.3367     3.04   0.002     1.448739    6.688508
  intensity2 |    .719789   .1441336     4.99   0.000     .4372924    1.002286
        info |   .0053115    .009495     0.56   0.576    -.0132983    .0239213
      pop_ln |   .5322649   .0907955     5.86   0.000     .3543091    .7102208
    gdppc_ln |   .4023507   .1620421     2.48   0.013     .0847539    .7199474
   gdppc_cng |   -.029941   .0216325    -1.38   0.166      -.07234     .012458
       y1992 |   .2060763   .6228049     0.33   0.741    -1.014599    1.426752
       y1993 |   .4824447    .572144     0.84   0.399    -.6389369    1.603826
       y1994 |  -.4253129   .5416072    -0.79   0.432    -1.486844    .6362177
       y1995 |   .0454887   .4984554     0.09   0.927     -.931466    1.022443
       y1996 |  -.2045775   .6040181    -0.34   0.735    -1.388431    .9792762
       y1997 |   .4704339   .6077028     0.77   0.439    -.7206418     1.66151
       y1998 |  -.0789975   .4919577    -0.16   0.872    -1.043217    .8852218
       y1999 |  -.8586204   .6270588    -1.37   0.171    -2.087633    .3703922
       y2000 |   .0611408   .5143918     0.12   0.905    -.9470487     1.06933
       y2001 |   .6836311   .4934569     1.39   0.166    -.2835266    1.650789
       y2002 |  -.0731853   .4320574    -0.17   0.865    -.9200023    .7736317
       y2003 |   .3035508   .4950093     0.61   0.540    -.6666497    1.273751
       y2004 |   .5938946   .4859469     1.22   0.222    -.3585438    1.546333
       y2005 |   -.172393   .5077226    -0.34   0.734    -1.167511     .822725
       y2006 |  -.2795899   .5437983    -0.51   0.607    -1.345415    .7862351
       y2007 |    .262242   .4208079     0.62   0.533    -.5625263     1.08701
       y2008 |    .038026   .5276134     0.07   0.943    -.9960772    1.072129
       y2009 |    .397153   .6089147     0.65   0.514    -.7962979    1.590604
       y2010 |   .0815107   .5392858     0.15   0.880    -.9754701    1.138492
       y2011 |  -.1110865   .4311817    -0.26   0.797    -.9561871    .7340142
       y2012 |  -.0923489   .3648262    -0.25   0.800    -.8073951    .6226973
       y2013 |  -.3723754   .4693268    -0.79   0.428    -1.292239    .5474882
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -12.10316    2.00409    -6.04   0.000     -16.0311   -8.175211
-------------+----------------------------------------------------------------
    /lnalpha |  -.3249632    .277514                     -.8688806    .2189543
-------------+----------------------------------------------------------------
       alpha |    .722554   .2005188                      .4194208    1.244774
------------------------------------------------------------------------------

. 
. *store estimates
. est sto gdppc4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
      gdppc4 |      1,900  -923.337  -630.9072      34    1329.814   1518.501
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1329.814
. 
. ****Create TABLE 20 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab gdppc1 gdppc2 gdppc3 gdppc4 using apx_gdppc.tex, replace se aic obslas
> t r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" fix "CPJ Unconfirmed" ///
>  gdp_ln "GDP (ln)" gdppc_ln "GDP p/c (ln)" gdppc_cng "$\Delta$ GDP p/c" ) ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(note: file apx_gdppc.tex not found)
(output written to apx_gdppc.tex)

. 
. *Table 21: w/ homicide counts covariate
. 
. *Model 1: Full (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln hom_count  ///
>   y199* y2* , cluster(ccode)  nolog 
note: y1992 omitted because of collinearity
note: y1993 omitted because of collinearity
note: y1994 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,982
                                                Wald chi2(28)     =     637.35
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -714.04246               Pseudo R2         =     0.2748

                                (Std. Err. adjusted for 157 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3210079   .0724097    -4.43   0.000    -.4629283   -.1790874
     polity2 |   .0452267   .0231406     1.95   0.051    -.0001281    .0905814
  public_cor |   .6446893   .6386903     1.01   0.313    -.6071206    1.896499
 physical_vd |  -5.613463   .7765929    -7.23   0.000    -7.135557   -4.091369
  express_vd |   3.910278   .6646016     5.88   0.000     2.607682    5.212873
  intensity2 |   .9234999   .1487014     6.21   0.000     .6320504    1.214949
        info |   .0180075   .0063684     2.83   0.005     .0055257    .0304893
      pop_ln |   .1666269   .0822067     2.03   0.043     .0055048     .327749
   hom_count |   .0000396   .0000116     3.40   0.001     .0000168    .0000623
       y1992 |          0  (omitted)
       y1993 |          0  (omitted)
       y1994 |          0  (omitted)
       y1995 |   .0598193   .5421449     0.11   0.912    -1.002765    1.122404
       y1996 |  -.6834441   .6338475    -1.08   0.281    -1.925762    .5588741
       y1997 |   .2084569   .5057576     0.41   0.680    -.7828097    1.199724
       y1998 |  -.3773955   .5255699    -0.72   0.473    -1.407494    .6527027
       y1999 |  -1.465382   .7277763    -2.01   0.044    -2.891798    -.038967
       y2000 |  -.5255585   .5107421    -1.03   0.303    -1.526595    .4754776
       y2001 |   .4605194   .4377785     1.05   0.293    -.3975107     1.31855
       y2002 |  -.4753788   .4217827    -1.13   0.260    -1.302058    .3513001
       y2003 |  -.0299514   .4260088    -0.07   0.944    -.8649133    .8050105
       y2004 |   .2024214   .4206902     0.48   0.630    -.6221162    1.026959
       y2005 |  -.2608858   .4444657    -0.59   0.557    -1.132023     .610251
       y2006 |  -.4843893   .4716001    -1.03   0.304    -1.408709    .4399299
       y2007 |  -.1720352   .3561233    -0.48   0.629    -.8700241    .5259537
       y2008 |  -.5028472   .4402495    -1.14   0.253     -1.36572     .360026
       y2009 |   .2737749   .4916661     0.56   0.578     -.689873    1.237423
       y2010 |  -.0982774   .4611169    -0.21   0.831     -1.00205    .8054951
       y2011 |  -.2738043   .4042341    -0.68   0.498    -1.066089      .51848
       y2012 |  -.1382551   .3440132    -0.40   0.688    -.8125085    .5359984
       y2013 |  -.4340414   .4833122    -0.90   0.369    -1.381316    .5132332
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -4.955231   1.952534    -2.54   0.011    -8.782129   -1.128334
-------------+----------------------------------------------------------------
    /lnalpha |  -.2591009   .2405794                     -.7306277     .212426
-------------+----------------------------------------------------------------
       alpha |   .7717452    .185666                      .4816066    1.236675
------------------------------------------------------------------------------

. 
. *store estimates
. est sto count1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
      count1 |      1,982 -984.6439  -714.0425      30    1488.085   1655.841
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1488.085
. 
. *Model 2: Auto (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln hom_count  ///
>     y199* y2*   if durable2==0, cluster(ccode)
note: y1992 omitted because of collinearity
note: y1993 omitted because of collinearity
note: y1994 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Fitting Poisson model:

Iteration 0:   log pseudolikelihood =  -68.95002  
Iteration 1:   log pseudolikelihood = -53.499434  
Iteration 2:   log pseudolikelihood = -50.486555  
Iteration 3:   log pseudolikelihood = -44.088747  
Iteration 4:   log pseudolikelihood = -43.984912  
Iteration 5:   log pseudolikelihood = -43.973976  
Iteration 6:   log pseudolikelihood = -43.972139  
Iteration 7:   log pseudolikelihood = -43.971706  
Iteration 8:   log pseudolikelihood = -43.971608  
Iteration 9:   log pseudolikelihood = -43.971586  
Iteration 10:  log pseudolikelihood = -43.971583  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -71.027723  
Iteration 1:   log pseudolikelihood = -71.025375  
Iteration 2:   log pseudolikelihood = -71.025346  
Iteration 3:   log pseudolikelihood = -71.025346  

Fitting full model:

Iteration 0:   log pseudolikelihood = -71.025346  
Iteration 1:   log pseudolikelihood = -51.362782  
Iteration 2:   log pseudolikelihood = -44.822565  
Iteration 3:   log pseudolikelihood = -44.154198  
Iteration 4:   log pseudolikelihood = -44.008993  
Iteration 5:   log pseudolikelihood =  -43.97883  
Iteration 6:   log pseudolikelihood = -43.973223  
Iteration 7:   log pseudolikelihood = -43.971963  
Iteration 8:   log pseudolikelihood = -43.971664  
Iteration 9:   log pseudolikelihood =   -43.9716  
Iteration 10:  log pseudolikelihood = -43.971586  (not concave)
Iteration 11:  log pseudolikelihood = -43.971586  (not concave)
Iteration 12:  log pseudolikelihood = -43.971586  

Negative binomial regression                    Number of obs     =        217
                                                Wald chi2(19)     =          .
Dispersion           = mean                     Prob > chi2       =          .
Log pseudolikelihood = -43.971586               Pseudo R2         =     0.3809

                                 (Std. Err. adjusted for 29 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.1760295   .2450743    -0.72   0.473    -.6563663    .3043073
     polity2 |   .1598334   .2807988     0.57   0.569    -.3905221    .7101889
  public_cor |   .2542249   1.514956     0.17   0.867    -2.715035    3.223484
 physical_vd |  -3.844739   1.771157    -2.17   0.030    -7.316144   -.3733351
  express_vd |   3.712843   1.910614     1.94   0.052    -.0318924    7.457579
  intensity2 |    1.88388   .3873117     4.86   0.000     1.124763    2.642997
        info |   .0473542   .0183931     2.57   0.010     .0113044    .0834039
      pop_ln |  -.3205112   .2453743    -1.31   0.191    -.8014361    .1604137
   hom_count |    .000128   .0000753     1.70   0.089    -.0000196    .0002757
       y1992 |          0  (omitted)
       y1993 |          0  (omitted)
       y1994 |          0  (omitted)
       y1995 |   16.84197   1.308038    12.88   0.000     14.27827    19.40568
       y1996 |   14.99627   1.132052    13.25   0.000     12.77749    17.21505
       y1997 |   16.20735   1.380977    11.74   0.000     13.50068    18.91402
       y1998 |   1.550904   1.129356     1.37   0.170    -.6625939    3.764402
       y1999 |   .7302668   1.418181     0.51   0.607    -2.049317     3.50985
       y2000 |   15.36389    1.13023    13.59   0.000     13.14868     17.5791
       y2001 |   1.152992   1.380476     0.84   0.404    -1.552692    3.858676
       y2002 |   13.59715   1.077636    12.62   0.000     11.48502    15.70927
       y2003 |   15.23845   1.200345    12.70   0.000     12.88582    17.59108
       y2004 |   14.60491   1.059139    13.79   0.000     12.52904    16.68078
       y2005 |   14.49213   1.023575    14.16   0.000     12.48596    16.49829
       y2006 |   15.55055    1.01047    15.39   0.000     13.57006    17.53103
       y2007 |   14.88015   1.152573    12.91   0.000     12.62115    17.13915
       y2008 |   .2757888   .6401294     0.43   0.667    -.9788418    1.530419
       y2009 |   15.48836   1.357497    11.41   0.000     12.82772    18.14901
       y2010 |   14.05742    1.65301     8.50   0.000     10.81758    17.29725
       y2011 |   1.103358   .7702592     1.43   0.152    -.4063219    2.613039
       y2012 |  -.1064739          .        .       .            .           .
       y2013 |   .3641052   .6209419     0.59   0.558    -.8529185    1.581129
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -13.69519   6.192752    -2.21   0.027    -25.83276   -1.557621
-------------+----------------------------------------------------------------
    /lnalpha |  -17.16772   5.295265                     -27.54625   -6.789188
-------------+----------------------------------------------------------------
       alpha |   3.50e-08   1.85e-07                      1.09e-12    .0011259
------------------------------------------------------------------------------

. 
. *store estimates
. est sto count2

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
      count2 |        217 -71.02535  -43.97159      21    129.9432    200.921
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=129.943
. 
. *Model 3: Ano (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln  hom_count  ///
>   y199* y2* if durable2==1, cluster(ccode)  nolog
note: y1992 omitted because of collinearity
note: y1993 omitted because of collinearity
note: y1994 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =        391
                                                Wald chi2(28)     =   10062.24
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -153.16986               Pseudo R2         =     0.2560

                                 (Std. Err. adjusted for 61 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3293357   .1363702    -2.42   0.016    -.5966164   -.0620551
     polity2 |   .0726969   .0481929     1.51   0.131    -.0217595    .1671533
  public_cor |  -.8652243   .9954729    -0.87   0.385    -2.816315    1.085867
 physical_vd |  -4.035612   1.164411    -3.47   0.001    -6.317815   -1.753408
  express_vd |   3.762113   1.032857     3.64   0.000     1.737751    5.786475
  intensity2 |   .7207795   .2140205     3.37   0.001      .301307    1.140252
        info |   .0179946   .0109835     1.64   0.101    -.0035326    .0395218
      pop_ln |   .3830245   .1810701     2.12   0.034     .0281336    .7379155
   hom_count |   .0000114   .0000319     0.36   0.720    -.0000511     .000074
       y1992 |          0  (omitted)
       y1993 |          0  (omitted)
       y1994 |          0  (omitted)
       y1995 |   .8507963   .8165612     1.04   0.297    -.7496343    2.451227
       y1996 |  -.1652651   1.461931    -0.11   0.910    -3.030598    2.700067
       y1997 |  -13.91854   .8095412   -17.19   0.000    -15.50521   -12.33187
       y1998 |   .3679566   1.207821     0.30   0.761    -1.999329    2.735242
       y1999 |  -14.26207   .7825908   -18.22   0.000    -15.79592   -12.72822
       y2000 |   -14.1433   .7966162   -17.75   0.000    -15.70464   -12.58196
       y2001 |   .9040069   .9980562     0.91   0.365    -1.052147    2.860161
       y2002 |   .6988812   .7134787     0.98   0.327    -.6995114    2.097274
       y2003 |  -.1342783   .9481747    -0.14   0.887    -1.992666     1.72411
       y2004 |   .5299804   .9464404     0.56   0.575    -1.325009     2.38497
       y2005 |    .641604   .6825915     0.94   0.347    -.6962508    1.979459
       y2006 |   .0948314   .7110374     0.13   0.894    -1.298776    1.488439
       y2007 |   .0854492   .6265065     0.14   0.892    -1.142481    1.313379
       y2008 |   .4312618   .7465432     0.58   0.563    -1.031936     1.89446
       y2009 |   .2475157     .80215     0.31   0.758    -1.324669    1.819701
       y2010 |   .7676161   .6919223     1.11   0.267    -.5885267    2.123759
       y2011 |   .5305304   .8436841     0.63   0.529     -1.12306    2.184121
       y2012 |   .4683362   .6764739     0.69   0.489    -.8575282    1.794201
       y2013 |   .4599175   .8752817     0.53   0.599    -1.255603    2.175438
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -8.737388   3.517318    -2.48   0.013     -15.6312   -1.843572
-------------+----------------------------------------------------------------
    /lnalpha |   -58.9424          .                             .           .
-------------+----------------------------------------------------------------
       alpha |   2.52e-26          .                             .           .
------------------------------------------------------------------------------

. 
. *store estimates
. est sto count3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
      count3 |        391 -205.8736  -153.1699      29    364.3397   479.4322
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=364.34
.   
. *Model 4: Demo (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln hom_count  ///
>   y199* y2* if durable2==2, cluster(ccode)  nolog
note: y1992 omitted because of collinearity
note: y1993 omitted because of collinearity
note: y1994 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,374
                                                Wald chi2(28)     =     542.00
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -476.88558               Pseudo R2         =     0.3168

                                (Std. Err. adjusted for 100 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.1148428   .1033297    -1.11   0.266    -.3173653    .0876797
     polity2 |  -.3931189   .1416799    -2.77   0.006    -.6708063   -.1154314
  public_cor |   .6281909   .6805826     0.92   0.356    -.7057266    1.962108
 physical_vd |  -5.469026   1.069019    -5.12   0.000    -7.564264   -3.373787
  express_vd |   4.192306   1.288809     3.25   0.001     1.666287    6.718324
  intensity2 |   .7659826   .1981147     3.87   0.000     .3776849     1.15428
        info |   .0203814   .0086121     2.37   0.018     .0035019    .0372608
      pop_ln |   .3114202   .0925779     3.36   0.001     .1299709    .4928696
   hom_count |   .0000302   .0000112     2.70   0.007     8.30e-06    .0000521
       y1992 |          0  (omitted)
       y1993 |          0  (omitted)
       y1994 |          0  (omitted)
       y1995 |  -.3096879    .606455    -0.51   0.610    -1.498318    .8789421
       y1996 |  -.8370868   .7792702    -1.07   0.283    -2.364428    .6902547
       y1997 |   .2094022   .6171716     0.34   0.734    -1.000232    1.419036
       y1998 |  -.5394221   .5571131    -0.97   0.333    -1.631344    .5524995
       y1999 |  -1.465234   .7577309    -1.93   0.053    -2.950359    .0198913
       y2000 |  -.5156142   .5659144    -0.91   0.362    -1.624786    .5935576
       y2001 |   .5521513   .4925522     1.12   0.262    -.4132333    1.517536
       y2002 |  -.6369566   .5880343    -1.08   0.279    -1.789483    .5155695
       y2003 |  -.2294814   .5067223    -0.45   0.651    -1.222639     .763676
       y2004 |   .0933393   .4968934     0.19   0.851    -.8805538    1.067232
       y2005 |  -.5709481   .5229819    -1.09   0.275    -1.595974    .4540776
       y2006 |  -.7929864    .534132    -1.48   0.138    -1.839866    .2538931
       y2007 |  -.1477206   .4070766    -0.36   0.717     -.945576    .6501349
       y2008 |  -.4607755   .5088253    -0.91   0.365    -1.458055    .5365038
       y2009 |    .309294   .5714424     0.54   0.588    -.8107125      1.4293
       y2010 |  -.3088581   .5564996    -0.56   0.579    -1.399577    .7818611
       y2011 |  -.4148393   .4511986    -0.92   0.358    -1.299172    .4694936
       y2012 |  -.3842857   .3931255    -0.98   0.328    -1.154798    .3862262
       y2013 |  -.5913015   .5499591    -1.08   0.282    -1.669202    .4865986
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -4.743183   2.140718    -2.22   0.027    -8.938912   -.5474529
-------------+----------------------------------------------------------------
    /lnalpha |  -.2961164   .2010159                     -.6901003    .0978674
-------------+----------------------------------------------------------------
       alpha |   .7437008   .1494957                      .5015257    1.102817
------------------------------------------------------------------------------

. 
. *store estimates
. est sto count4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
      count4 |      1,374 -698.0545  -476.8856      30    1013.771   1170.536
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1013.771
. 
. ****Create TABLE 21 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab count1 count2 count3 count4 using apx_count.tex, replace se aic obslas
> t r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" fix "CPJ Unconfirmed" ///
>  hom_count "Homicides (count)" ) ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(note: file apx_count.tex not found)
(output written to apx_count.tex)

.  
. *Table 21: w/ homicide rates covariate
.  
. *Model 1: Full (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln hom_rate  ///
>   y199* y2* , cluster(ccode)  nolog 
note: y1992 omitted because of collinearity
note: y1993 omitted because of collinearity
note: y1994 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      2,032
                                                Wald chi2(28)     =     834.10
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -733.64077               Pseudo R2         =     0.2667

                                (Std. Err. adjusted for 157 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3052795   .0808955    -3.77   0.000    -.4638318   -.1467273
     polity2 |   .0444519   .0238256     1.87   0.062    -.0022454    .0911493
  public_cor |   .5499155    .645869     0.85   0.395    -.7159645    1.815796
 physical_vd |  -5.048292   .8063059    -6.26   0.000    -6.628622   -3.467962
  express_vd |   3.804783   .6651721     5.72   0.000      2.50107    5.108497
  intensity2 |   .9274029     .14819     6.26   0.000     .6369558     1.21785
        info |   .0150086   .0056659     2.65   0.008     .0039036    .0261137
      pop_ln |   .4107799   .1000266     4.11   0.000     .2147314    .6068285
    hom_rate |   .0176448   .0055699     3.17   0.002      .006728    .0285615
       y1992 |          0  (omitted)
       y1993 |          0  (omitted)
       y1994 |          0  (omitted)
       y1995 |   -.112977   .5428984    -0.21   0.835    -1.177038    .9510843
       y1996 |   -.761716   .6422951    -1.19   0.236    -2.020591    .4971593
       y1997 |   .1448825   .5359585     0.27   0.787    -.9055769    1.195342
       y1998 |  -.5322961   .5359342    -0.99   0.321    -1.582708    .5181156
       y1999 |  -1.630385   .7466445    -2.18   0.029    -3.093781   -.1669883
       y2000 |  -.5378054   .5278362    -1.02   0.308    -1.572345    .4967345
       y2001 |   .2464633   .4262204     0.58   0.563    -.5889135     1.08184
       y2002 |  -.4836673   .4468618    -1.08   0.279      -1.3595    .3921656
       y2003 |  -.1791502   .4446883    -0.40   0.687    -1.050723    .6924228
       y2004 |   .1217814   .4150055     0.29   0.769    -.6916145    .9351772
       y2005 |  -.3047341   .4493447    -0.68   0.498    -1.185433    .5759654
       y2006 |  -.5529442   .4915849    -1.12   0.261    -1.516433    .4105444
       y2007 |  -.2470872   .3499641    -0.71   0.480    -.9330042    .4388298
       y2008 |  -.5266189   .4517277    -1.17   0.244    -1.411989    .3587511
       y2009 |   .1447612   .5092504     0.28   0.776    -.8533512    1.142874
       y2010 |  -.1729873   .4481886    -0.39   0.700    -1.051421    .7054463
       y2011 |  -.3004277   .3840621    -0.78   0.434    -1.053176    .4523202
       y2012 |  -.1257325   .3232959    -0.39   0.697    -.7593808    .5079158
       y2013 |  -.4001711   .4536661    -0.88   0.378     -1.28934    .4889981
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.087605   2.251475    -4.04   0.000    -13.50042   -4.674794
-------------+----------------------------------------------------------------
    /lnalpha |  -.2445949   .2799528                     -.7932924    .3041025
-------------+----------------------------------------------------------------
       alpha |   .7830216   .2192091                       .452353    1.355408
------------------------------------------------------------------------------

. 
. *store estimates
. est sto rate1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       rate1 |      2,032 -1000.439  -733.6408      30    1527.282   1695.785
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1527.282
. 
. *Model 2: Auto (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln hom_rate  ///
>     y199* y2* if durable2==0, cluster(ccode) nolog
note: y1992 omitted because of collinearity
note: y1993 omitted because of collinearity
note: y1994 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =        226
                                                Wald chi2(17)     =          .
Dispersion           = mean                     Prob > chi2       =          .
Log pseudolikelihood = -44.910397               Pseudo R2         =     0.3748

                                 (Std. Err. adjusted for 29 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.0780484    .250235    -0.31   0.755    -.5685001    .4124033
     polity2 |   .1103713   .2707301     0.41   0.684      -.42025    .6409926
  public_cor |    .272522   1.449719     0.19   0.851    -2.568876     3.11392
 physical_vd |  -3.766172   1.817631    -2.07   0.038    -7.328663   -.2036815
  express_vd |   4.095513   2.172037     1.89   0.059    -.1616013    8.352627
  intensity2 |   1.771952   .4060119     4.36   0.000     .9761836    2.567721
        info |   .0477074   .0203787     2.34   0.019     .0077659     .087649
      pop_ln |    .036923   .2017435     0.18   0.855    -.3584869    .4323329
    hom_rate |   .0487817   .0528475     0.92   0.356    -.0547974    .1523609
       y1992 |          0  (omitted)
       y1993 |          0  (omitted)
       y1994 |          0  (omitted)
       y1995 |   17.71623   1.460562    12.13   0.000     14.85358    20.57888
       y1996 |   15.87633   1.260611    12.59   0.000     13.40558    18.34709
       y1997 |   16.92443   1.465961    11.54   0.000      14.0512    19.79766
       y1998 |   .9422041   1.292692     0.73   0.466    -1.591425    3.475833
       y1999 |   .0562938   1.476354     0.04   0.970    -2.837307    2.949895
       y2000 |   15.74426   1.067891    14.74   0.000     13.65123    17.83729
       y2001 |   .3872704   1.195448     0.32   0.746    -1.955764    2.730305
       y2002 |   14.23226   1.022217    13.92   0.000     12.22875    16.23577
       y2003 |   15.87023   1.218443    13.03   0.000     13.48212    18.25833
       y2004 |   15.20041   1.102896    13.78   0.000     13.03878    17.36205
       y2005 |   15.15727   1.103889    13.73   0.000     12.99369    17.32086
       y2006 |   16.06823   1.111411    14.46   0.000      13.8899    18.24655
       y2007 |   15.32501   1.246085    12.30   0.000     12.88273     17.7673
       y2008 |   -.425415   .7582736    -0.56   0.575    -1.911604    1.060774
       y2009 |   15.90291   1.346185    11.81   0.000     13.26444    18.54138
       y2010 |   14.54209   1.662488     8.75   0.000     11.28367     17.8005
       y2011 |     .34892   .7506867     0.46   0.642    -1.122399    1.820239
       y2012 |  -.6726768      .4268    -1.58   0.115     -1.50919    .1638359
       y2013 |  -.2726963   .6171966    -0.44   0.659    -1.482379    .9369869
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -20.93813   6.321826    -3.31   0.001    -33.32868   -8.547579
-------------+----------------------------------------------------------------
    /lnalpha |  -33.69652          .                             .           .
-------------+----------------------------------------------------------------
       alpha |   2.32e-15          .                             .           .
------------------------------------------------------------------------------

. 
. *store estimates
. est sto rate2

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       rate2 |        226  -71.8352   -44.9104      18    125.8208   187.3904
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=125.821 (but changes, for some reason)
. 
. *Model 3: Ano (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln  hom_rate  ///
>   y199* y2* if durable2==1, cluster(ccode)  nolog
note: y1992 omitted because of collinearity
note: y1993 omitted because of collinearity
note: y1994 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =        407
                                                Wald chi2(28)     =    8307.03
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -154.62971               Pseudo R2         =     0.2567

                                 (Std. Err. adjusted for 63 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3220015   .1385811    -2.32   0.020    -.5936155   -.0503874
     polity2 |   .0717043   .0489029     1.47   0.143    -.0241437    .1675522
  public_cor |  -.7396067   1.001704    -0.74   0.460     -2.70291    1.223697
 physical_vd |  -3.984066   1.136996    -3.50   0.000    -6.212538   -1.755594
  express_vd |   3.927169   1.056115     3.72   0.000     1.857221    5.997116
  intensity2 |   .7590639   .2029757     3.74   0.000     .3612388    1.156889
        info |   .0208313   .0106635     1.95   0.051    -.0000688    .0417313
      pop_ln |   .4177575   .1484312     2.81   0.005     .1268376    .7086774
    hom_rate |  -.0033916   .0243661    -0.14   0.889    -.0511483     .044365
       y1992 |          0  (omitted)
       y1993 |          0  (omitted)
       y1994 |          0  (omitted)
       y1995 |   .9659779   .7882895     1.23   0.220    -.5790411    2.510997
       y1996 |  -.0855229   1.468152    -0.06   0.954    -2.963048    2.792002
       y1997 |  -14.95567   .8117749   -18.42   0.000    -16.54672   -13.36462
       y1998 |   .2603045   1.186793     0.22   0.826    -2.065767    2.586376
       y1999 |  -15.78443   .7279647   -21.68   0.000    -17.21122   -14.35765
       y2000 |  -15.72354   .8473573   -18.56   0.000    -17.38433   -14.06275
       y2001 |   .3138241   1.064811     0.29   0.768    -1.773167    2.400815
       y2002 |   .4565049   .7518555     0.61   0.544    -1.017105    1.930115
       y2003 |   -.089613   .9373922    -0.10   0.924    -1.926868    1.747642
       y2004 |   .5534709   .9292573     0.60   0.551     -1.26784    2.374782
       y2005 |   .6957037   .6516654     1.07   0.286     -.581537    1.972944
       y2006 |   .1047987   .6860864     0.15   0.879    -1.239906    1.449503
       y2007 |   .0887404   .6183347     0.14   0.886    -1.123173    1.300654
       y2008 |   .4866715   .7193992     0.68   0.499    -.9233251    1.896668
       y2009 |   .2426305   .7864013     0.31   0.758    -1.298688    1.783949
       y2010 |   .7939493   .6744948     1.18   0.239    -.5280361    2.115935
       y2011 |   .5472626   .8332359     0.66   0.511     -1.08585    2.180375
       y2012 |   .4900358   .6712944     0.73   0.465     -.825677    1.805749
       y2013 |   .4556669   .8754869     0.52   0.603    -1.260256     2.17159
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.682845   2.540095    -3.81   0.000    -14.66134   -4.704351
-------------+----------------------------------------------------------------
    /lnalpha |  -14.04166   9.452022                     -32.56728    4.483965
-------------+----------------------------------------------------------------
       alpha |   7.98e-07   7.54e-06                      7.18e-15    88.58519
------------------------------------------------------------------------------

. 
. *store estimates
. est sto rate3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       rate3 |        407 -208.0294  -154.6297      30    369.2594   489.5238
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=369.259
. 
. *Model 4: Demo (NBREG)
. nbreg confirmed seq_ln polity public_cor physical_vd express_vd intensity2 in
> fo pop_ln hom_rate  ///
>   y199* y2* if durable2==2, cluster(ccode)  nolog
note: y1992 omitted because of collinearity
note: y1993 omitted because of collinearity
note: y1994 omitted because of collinearity
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,399
                                                Wald chi2(28)     =     604.25
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -489.74274               Pseudo R2         =     0.3112

                                (Std. Err. adjusted for 100 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.1121771   .1160113    -0.97   0.334    -.3395551    .1152009
     polity2 |  -.4364778   .1460727    -2.99   0.003     -.722775   -.1501805
  public_cor |   .3035465   .6681455     0.45   0.650    -1.005995    1.613088
 physical_vd |  -5.023672   1.096846    -4.58   0.000    -7.173452   -2.873893
  express_vd |   4.089284    1.35178     3.03   0.002     1.439844    6.738723
  intensity2 |   .7515842    .183787     4.09   0.000     .3913683      1.1118
        info |   .0168997   .0077896     2.17   0.030     .0016324     .032167
      pop_ln |   .5328457   .1034095     5.15   0.000     .3301668    .7355246
    hom_rate |   .0125214   .0071535     1.75   0.080    -.0014992     .026542
       y1992 |          0  (omitted)
       y1993 |          0  (omitted)
       y1994 |          0  (omitted)
       y1995 |  -.5550927    .613346    -0.91   0.365    -1.757229    .6470433
       y1996 |  -.9812974   .7538423    -1.30   0.193    -2.458801    .4962064
       y1997 |   .0662135   .6344248     0.10   0.917    -1.177236    1.309663
       y1998 |  -.7568871   .5925422    -1.28   0.201    -1.918248    .4044741
       y1999 |  -1.635896   .7927573    -2.06   0.039    -3.189672   -.0821205
       y2000 |  -.6345068   .5512989    -1.15   0.250    -1.715033    .4460192
       y2001 |   .3200335   .4606921     0.69   0.487    -.5829064    1.222973
       y2002 |  -.6613987   .5424391    -1.22   0.223     -1.72456    .4017623
       y2003 |  -.4400663   .5408644    -0.81   0.416    -1.500141    .6200084
       y2004 |  -.0376211   .4811193    -0.08   0.938    -.9805977    .9053555
       y2005 |  -.6778659    .511114    -1.33   0.185    -1.679631    .3238991
       y2006 |  -.8929471   .5411133    -1.65   0.099     -1.95351    .1676155
       y2007 |  -.2726564   .3846855    -0.71   0.478    -1.026626    .4813133
       y2008 |  -.5435957   .5120356    -1.06   0.288    -1.547167    .4599756
       y2009 |   .1483616   .5922733     0.25   0.802    -1.012473    1.309196
       y2010 |  -.4379136   .5379945    -0.81   0.416    -1.492363    .6165363
       y2011 |  -.4594528   .4204446    -1.09   0.274    -1.283509    .3646036
       y2012 |  -.3971324   .3684081    -1.08   0.281    -1.119199    .3249341
       y2013 |  -.5649118    .503087    -1.12   0.261    -1.550944    .4211207
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -7.875375   2.453556    -3.21   0.001    -12.68426   -3.066494
-------------+----------------------------------------------------------------
    /lnalpha |  -.2894581   .2147411                      -.710343    .1314268
-------------+----------------------------------------------------------------
       alpha |   .7486691   .1607701                      .4914756    1.140454
------------------------------------------------------------------------------

. 
. *store estimates
. est sto rate4

. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       rate4 |      1,399 -711.0041  -489.7427      30    1039.485   1196.791
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1039.485
. 
. ****Create TABLE 22 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab rate1 rate2 rate3 rate4 using apx_rate.tex, replace se aic obslast r2 
> ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" fix "CPJ Unconfirmed" ///
>  hom_rate "Homicides (rate)" ) ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(note: file apx_rate.tex not found)
(output written to apx_rate.tex)

. 
.  ******************************
.  *****IMPUTED MISSING DATA*****
.  ******************************
. 
. *Get sense of how much missingness exists
.  
. sum seq_ln if polity!=. & inrange(year,1995,2014)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      seq_ln |      3,210    2.758806    1.126142          0   5.370638

. *n=3124; homicide data (n) drops it down to 1,982 (table 21) and 2,032 (table
>  22). 
. * Over 1/3 lost.
. 
. *Impute Homicide Counts*
. 
. *Setup for imputation
. mi set mlong

. 
. *Missingness table using model variables
. mi misstable patterns confirmed seq_ln polity public_cor physical_vd ///
> express_vd intensity2 info pop_ln hom_count  

             Missing-value patterns
               (1 means complete)

              |   Pattern
    Percent   |  1  2  3  4    5  6  7  8    9
  ------------+--------------------------------
       47%    |  1  1  1  1    1  1  1  1    1
              |
       38     |  1  1  1  1    1  1  1  1    0
        8     |  1  0  0  0    0  0  0  0    0
        4     |  0  1  1  1    1  1  1  0    0
        1     |  1  1  1  1    0  0  0  1    0
       <1     |  1  1  1  0    1  1  1  1    0
       <1     |  1  1  1  1    0  0  0  1    1
       <1     |  0  1  1  1    1  1  1  0    1
       <1     |  1  1  0  0    1  1  1  1    0
       <1     |  1  1  1  0    1  1  1  1    1
       <1     |  1  1  0  0    1  1  1  1    1
       <1     |  1  1  0  1    1  1  1  1    0
       <1     |  0  0  0  0    0  0  0  0    0
  ------------+--------------------------------
      100%    |

  Variables are  (1) polity2  (2) intensity2  (3) pop_ln  (4) info
                 (5) express_vd  (6) physical_vd  (7) public_cor  (8) seq_ln
                 (9) hom_count

. 
. *Tell Stata which variables you're imputing and which you're not imputing
. 
. *NOT IMPUTING
. mi register regular confirmed seq_ln polity intensity2 durable2 pop_ln ///
>  info express_vd physical_vd public_cor  y199* y2*

. 
. *IMPUTING
. mi register imputed hom_count
(2207 m=0 obs. now marked as incomplete)

. 
. *Imputation mode: use all right-hand side variables to impute  hom_count. 20 
> diff. datasets
. mi impute mvn   hom_count ///
>   = seq_ln polity intensity2  pop_ln info express_vd physical_vd public_cor, 
> add(20) rseed(623)  force

Performing EM optimization:
note: 1604 observations omitted from EM estimation because of all imputation
      variables missing
  observed log likelihood =   -18005.6 at iteration 1

Performing MCMC data augmentation ... 

Multivariate imputation                     Imputations =       20
Multivariate normal regression                    added =       20
Imputed: m=1 through m=20                       updated =        0

Prior: uniform                               Iterations =     2000
                                                burn-in =      100
                                                between =      100

------------------------------------------------------------------
                   |               Observations per m             
                   |----------------------------------------------
          Variable |   Complete   Incomplete   Imputed |     Total
-------------------+-----------------------------------+----------
         hom_count |       2045         2207      1604 |      4252
------------------------------------------------------------------
(complete + incomplete = total; imputed is the minimum across m
 of the number of filled-in observations.)

Note: Right-hand-side variables (or weights) have missing values;
      model parameters estimated using listwise deletion.

. 
. *Summarize hom_count in different datasets (0= original, 1-20 are imputed dat
> asets)
. mi xeq 0 1 5 10 15 20: summarize hom_count   

m=0 data:
-> summarize hom_count

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   hom_count |      2,045    2536.498    6719.679          1      57091

m=1 data:
-> summarize hom_count

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   hom_count |      3,649    2662.401    6868.961  -22050.02      57091

m=5 data:
-> summarize hom_count

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   hom_count |      3,649    2779.827    6930.145   -19089.1      57091

m=10 data:
-> summarize hom_count

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   hom_count |      3,649    2767.554    6958.039  -19150.55      57091

m=15 data:
-> summarize hom_count

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   hom_count |      3,649    2667.218    6882.938  -24040.31      57091

m=20 data:
-> summarize hom_count

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   hom_count |      3,649    2846.807    6841.499  -18564.56      57091

.  
. *****************************
. *Analysis using imputed data*
. *****************************
. 
. *Table 23: w/ homicide counts covariate (imputed)
. 
. *Model 1: Full (NBREG)
. mi estimate, post: nbreg confirmed seq_ln polity public_cor physical_vd expre
> ss_vd intensity2 info pop_ln hom_count  ///
>   y199* y2* , cluster(ccode)  nolog 

Multiple-imputation estimates                   Imputations       =         20
Negative binomial regression                    Number of obs     =      3,586
                                                Average RVI       =     0.0358
                                                Largest FMI       =     0.2221
DF adjustment:   Large sample                   DF:     min       =     399.13
                                                        avg       = 456,637.82
                                                        max       = 1398286.34
Model F test:       Equal FMI                   F(  31,524336.9)  =      19.20
Within VCE type:       Robust                   Prob > F          =     0.0000

                               (Within VCE adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
   confirmed |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3306575   .0853366    -3.87   0.000     -.497915   -.1633999
     polity2 |   .0003559   .0262815     0.01   0.989     -.051155    .0518668
  public_cor |   .6450358   .5444595     1.18   0.236    -.4220875    1.712159
 physical_vd |  -4.702666   .7098288    -6.63   0.000    -6.093906   -3.311426
  express_vd |   3.495238   .7047705     4.96   0.000     2.113909    4.876568
  intensity2 |     1.2594   .1310004     9.61   0.000     1.002633    1.516167
        info |   .0306852   .0061647     4.98   0.000     .0186025    .0427679
      pop_ln |    .282339   .0614701     4.59   0.000     .1618129    .4028651
   hom_count |   .0000314   .0000107     2.93   0.004     .0000104    .0000525
       y1992 |   .0522375   .4097965     0.13   0.899    -.7509647    .8554397
       y1993 |    .635604   .3736331     1.70   0.089    -.0967044    1.367912
       y1994 |   .6943392   .4024127     1.73   0.084    -.0943978    1.483076
       y1995 |   .2419301   .3787769     0.64   0.523    -.5004674    .9843277
       y1996 |   -.405367   .4067775    -1.00   0.319    -1.202643    .3919096
       y1997 |  -.3486982   .3897282    -0.89   0.371    -1.112554    .4151579
       y1998 |  -.2424336   .3683598    -0.66   0.510    -.9644094    .4795422
       y1999 |  -.2821406   .4903744    -0.58   0.565    -1.243263    .6789822
       y2000 |  -.3041965   .4104723    -0.74   0.459    -1.108709     .500316
       y2001 |   .1836014   .3880191     0.47   0.636    -.5769032    .9441059
       y2002 |  -.6717023   .4122672    -1.63   0.103    -1.479732    .1363279
       y2003 |  -.0486444   .3414709    -0.14   0.887    -.7179163    .6206276
       y2004 |   .2763003   .3756833     0.74   0.462    -.4600271    1.012628
       y2005 |   -.069658   .3729606    -0.19   0.852    -.8006482    .6613323
       y2006 |  -.6282505   .3848773    -1.63   0.103    -1.382598    .1260966
       y2007 |   .0057059    .327159     0.02   0.986    -.6355183    .6469301
       y2008 |  -.4274322   .3780121    -1.13   0.258    -1.168323    .3134588
       y2009 |   .1678987   .3888163     0.43   0.666    -.5941681    .9299654
       y2010 |   .1417539   .4130203     0.34   0.731    -.6677517    .9512595
       y2011 |  -.1246377    .316087    -0.39   0.693    -.7441632    .4948878
       y2012 |   .2036768   .2744241     0.74   0.458    -.3343113    .7416649
       y2013 |   .1143995   .3255063     0.35   0.725    -.5236097    .7524087
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |   -8.04679   1.192726    -6.75   0.000    -10.38478   -5.708798
-------------+----------------------------------------------------------------
    /lnalpha |   .7154301   .2136631                      .2966548    1.134205
-------------+----------------------------------------------------------------
       alpha |   2.045066   .4369551                      1.345351    3.108702
------------------------------------------------------------------------------

. 
. *store estimates
. est sto count_imp1

. 
. *Model 2: Auto (NBREG)
. mi estimate, post:  nbreg confirmed seq_ln polity public_cor physical_vd expr
> ess_vd intensity2 info pop_ln hom_count  ///
>     y199* y2*   if durable2==0, cluster(ccode) nolog

Multiple-imputation estimates                   Imputations       =         20
Negative binomial regression                    Number of obs     =        597
                                                Average RVI       =     2.0187
                                                Largest FMI       =     0.8970
DF adjustment:   Large sample                   DF:     min       =      24.05
                                                        avg       =  44,577.15
                                                        max       = 618,341.55
Model F test:       Equal FMI                   F(  31, 1397.1)   =      95.45
Within VCE type:       Robust                   Prob > F          =     0.0000

                                (Within VCE adjusted for 52 clusters in ccode)
------------------------------------------------------------------------------
   confirmed |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4409015   .2055996    -2.14   0.032    -.8438778   -.0379252
     polity2 |   .0300512   .2080449     0.14   0.885    -.3777194    .4378217
  public_cor |  -1.024052    1.19601    -0.86   0.392     -3.36835    1.320246
 physical_vd |  -3.414843   1.421096    -2.40   0.016    -6.200259   -.6294282
  express_vd |   2.100013   1.766579     1.19   0.235    -1.362439    5.562466
  intensity2 |   1.975152   .3442544     5.74   0.000      1.30023    2.650074
        info |   .0374893   .0134067     2.80   0.005     .0112119    .0637667
      pop_ln |   .1519289   .1730825     0.88   0.381    -.1888911    .4927488
   hom_count |   .0000362   .0000529     0.68   0.497      -.00007    .0001424
       y1992 |  -.3161317   .9738992    -0.32   0.746    -2.225658    1.593395
       y1993 |    .277732   .9152257     0.30   0.762    -1.516601    2.072065
       y1994 |   .5693594   .8235246     0.69   0.489     -1.04597    2.184689
       y1995 |  -1.097151   1.130598    -0.97   0.332    -3.313253    1.118952
       y1996 |  -1.283169   1.362366    -0.94   0.346     -3.95345    1.387112
       y1997 |  -.6787458    1.00313    -0.68   0.499    -2.645161    1.287669
       y1998 |  -1.538843   .9951493    -1.55   0.122    -3.490247      .41256
       y1999 |   .0682764   1.149435     0.06   0.953    -2.184876    2.321429
       y2000 |  -1.491445   1.457622    -1.02   0.306    -4.348453    1.365563
       y2001 |  -.6974204   1.749466    -0.40   0.690    -4.126338    2.731497
       y2002 |  -2.655454   .9752554    -2.72   0.006    -4.567174   -.7437343
       y2003 |  -.4876089   1.236305    -0.39   0.693    -2.910897    1.935679
       y2004 |    -.70856   1.467328    -0.48   0.629    -3.584557    2.167437
       y2005 |  -.3239028   1.149993    -0.28   0.778     -2.57804    1.930234
       y2006 |   .2697642   1.098459     0.25   0.806    -1.883344    2.422873
       y2007 |   .5668944   1.281876     0.44   0.658    -1.945621     3.07941
       y2008 |  -20.45196     1.6956   -12.06   0.000    -23.95109   -16.95283
       y2009 |   .4388687   1.050034     0.42   0.676    -1.619386    2.497124
       y2010 |  -.5719929   1.571166    -0.36   0.716    -3.651499    2.507513
       y2011 |   .3098392    .888447     0.35   0.727    -1.432096    2.051774
       y2012 |   .5640815   .4556067     1.24   0.219    -.3410212    1.469184
       y2013 |   .3715733   .3441026     1.08   0.286    -.3200147    1.063161
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -4.850556   3.000632    -1.62   0.106    -10.73837    1.037256
-------------+----------------------------------------------------------------
    /lnalpha |   1.123608   .5445935                      .0562223    2.190994
-------------+----------------------------------------------------------------
       alpha |   3.075932   1.675132                      1.057833    8.944096
------------------------------------------------------------------------------

. 
. *store estimates
. est sto count_imp2

. 
. *Model 3: Ano (NBREG)
. mi estimate, post: nbreg confirmed seq_ln polity public_cor physical_vd expre
> ss_vd intensity2 info pop_ln  hom_count  ///
>   y199* y2* if durable2==1, cluster(ccode)  nolog

Multiple-imputation estimates                   Imputations       =         20
Negative binomial regression                    Number of obs     =      1,067
                                                Average RVI       =     0.0269
                                                Largest FMI       =     0.3619
DF adjustment:   Large sample                   DF:     min       =     152.02
                                                        avg       =   1.58e+07
                                                        max       =   2.01e+08
Model F test:       Equal FMI                   F(  31,818870.6)  =      13.70
Within VCE type:       Robust                   Prob > F          =     0.0000

                                (Within VCE adjusted for 86 clusters in ccode)
------------------------------------------------------------------------------
   confirmed |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.5893438   .1386768    -4.25   0.000    -.8611454   -.3175422
     polity2 |   .0306929    .038607     0.80   0.427    -.0449756    .1063614
  public_cor |    .346638   .7329028     0.47   0.636    -1.089826    1.783102
 physical_vd |    -4.2779   1.070723    -4.00   0.000    -6.376478   -2.179323
  express_vd |   3.786705   .8873634     4.27   0.000     2.047504    5.525905
  intensity2 |   1.220938    .159904     7.64   0.000     .9075075    1.534369
        info |   .0311119   .0078935     3.94   0.000     .0156409    .0465829
      pop_ln |   .2367177   .1091577     2.17   0.030     .0227365    .4506989
   hom_count |   8.42e-06   .0000188     0.45   0.656    -.0000288    .0000456
       y1992 |    .186872   .5557831     0.34   0.737    -.9024433    1.276187
       y1993 |   .8252017   .4403344     1.87   0.061    -.0378393    1.688243
       y1994 |    1.05109   .4792385     2.19   0.028     .1117995    1.990381
       y1995 |   .9232875   .4590303     2.01   0.044     .0236037    1.822971
       y1996 |   .0839945   .5821638     0.14   0.885    -1.057026    1.225015
       y1997 |  -1.350735    .616574    -2.19   0.028    -2.559198   -.1422719
       y1998 |   .0115349   .5838603     0.02   0.984     -1.13281     1.15588
       y1999 |   .2223707   .5739204     0.39   0.698    -.9024928    1.347234
       y2000 |   .1150648   .5720077     0.20   0.841     -1.00605    1.236179
       y2001 |  -.2650228   .7940902    -0.33   0.739    -1.821411    1.291365
       y2002 |  -.9967746    .699034    -1.43   0.154    -2.366856     .373307
       y2003 |    -.03958   .5734388    -0.07   0.945    -1.163499     1.08434
       y2004 |    .250819    .688312     0.36   0.716    -1.098248    1.599886
       y2005 |   .6524138   .5642739     1.16   0.248    -.4535427     1.75837
       y2006 |  -.5206636   .5441752    -0.96   0.339    -1.587227    .5459003
       y2007 |   .2736071   .5213909     0.52   0.600    -.7483007    1.295515
       y2008 |   .0101575   .5197255     0.02   0.984    -1.008486    1.028801
       y2009 |   .3712176    .501762     0.74   0.459    -.6122181    1.354653
       y2010 |    .661252   .5985329     1.10   0.269     -.511851    1.834355
       y2011 |    .210846   .3908173     0.54   0.590    -.5551425    .9768345
       y2012 |   .1923885   .4727986     0.41   0.684      -.73428    1.119057
       y2013 |   .8101921   .4895232     1.66   0.098    -.1492557     1.76964
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |   -7.26785    1.59357    -4.56   0.000    -10.39181   -4.143887
-------------+----------------------------------------------------------------
    /lnalpha |   .4923442   .2727357                     -.0422081    1.026896
-------------+----------------------------------------------------------------
       alpha |   1.636147   .4462358                      .9586703    2.792386
------------------------------------------------------------------------------

. 
. *store estimates
. est sto count_imp3

. 
. *Model 4: Demo (NBREG)
. mi estimate, post:  nbreg confirmed seq_ln polity public_cor physical_vd expr
> ess_vd intensity2 info pop_ln hom_count  ///
>   y199* y2* if durable2==2, cluster(ccode)  nolog

Multiple-imputation estimates                   Imputations       =         20
Negative binomial regression                    Number of obs     =      1,922
                                                Average RVI       =     0.0329
                                                Largest FMI       =     0.0653
DF adjustment:   Large sample                   DF:     min       =   4,515.90
                                                        avg       =   1.59e+07
                                                        max       =   7.90e+07
Model F test:       Equal FMI                   F(  31,677532.3)  =      19.19
Within VCE type:       Robust                   Prob > F          =     0.0000

                               (Within VCE adjusted for 106 clusters in ccode)
------------------------------------------------------------------------------
   confirmed |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   .0221027   .0993183     0.22   0.824    -.1725583    .2167638
     polity2 |  -.2987762   .1373676    -2.18   0.030    -.5680118   -.0295406
  public_cor |   .8796128   .7004426     1.26   0.209    -.4932296    2.252455
 physical_vd |  -6.292355   .8082898    -7.78   0.000    -7.876575   -4.708135
  express_vd |   4.699895   1.170289     4.02   0.000     2.406171     6.99362
  intensity2 |   .6309348   .1359064     4.64   0.000     .3645629    .8973067
        info |   .0254902   .0063776     4.00   0.000     .0129903    .0379901
      pop_ln |   .4168596   .0729976     5.71   0.000     .2737717    .5599474
   hom_count |   .0000231   .0000103     2.23   0.026     2.82e-06    .0000433
       y1992 |   .3272264   .6458598     0.51   0.612    -.9386384    1.593091
       y1993 |    .610547   .5762869     1.06   0.289    -.5189555     1.74005
       y1994 |  -.3591513   .5922366    -0.61   0.544    -1.519914    .8016115
       y1995 |  -.0528066   .5942473    -0.09   0.929    -1.217511    1.111897
       y1996 |  -.2812339   .6133295    -0.46   0.647    -1.483338      .92087
       y1997 |   .2276876   .5953938     0.38   0.702    -.9392629    1.394638
       y1998 |  -.2381351   .5011604    -0.48   0.635    -1.220391    .7441213
       y1999 |   -1.04765   .6231267    -1.68   0.093    -2.268956     .173656
       y2000 |  -.2095365   .5083859    -0.41   0.680    -1.205955    .7868818
       y2001 |   .5110577   .4772614     1.07   0.284    -.4243576    1.446473
       y2002 |  -.2761737   .4064698    -0.68   0.497    -1.072841    .5204935
       y2003 |   .0945791   .4358728     0.22   0.828    -.7597164    .9488745
       y2004 |   .3593206   .4779961     0.75   0.452    -.5775346    1.296176
       y2005 |  -.3193575   .5088569    -0.63   0.530    -1.316699    .6779838
       y2006 |  -.4473894   .5107396    -0.88   0.381    -1.448421    .5536419
       y2007 |   .1248326   .4306277     0.29   0.772    -.7191823    .9688475
       y2008 |  -.0523511   .5092393    -0.10   0.918    -1.050442    .9457397
       y2009 |   .5159575   .5729428     0.90   0.368    -.6069898    1.638905
       y2010 |  -.0276332   .5355858    -0.05   0.959    -1.077362    1.022096
       y2011 |  -.2255142   .4456908    -0.51   0.613    -1.099052    .6480238
       y2012 |  -.1710247   .3642951    -0.47   0.639    -.8850301    .5429807
       y2013 |  -.5046883   .5051504    -1.00   0.318    -1.494765    .4853885
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -8.173206   2.102763    -3.89   0.000    -12.29457   -4.051846
-------------+----------------------------------------------------------------
    /lnalpha |  -.2666467   .2332995                     -.7239053    .1906119
-------------+----------------------------------------------------------------
       alpha |   .7659436   .1786942                      .4848551     1.20999
------------------------------------------------------------------------------

. 
. *store estimates
. est sto count_imp4

. 
. ****Create TABLE 23 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab count_imp1 count_imp2 count_imp3 count_imp4 using apx_count_imp.tex, r
> eplace se aic obslast r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" fix "CPJ Unconfirmed" ///
>  hom_count "Homicides (count)" ) ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(note: file apx_count_imp.tex not found)
(output written to apx_count_imp.tex)

. 
. *Impute Homicide Rates*
. 
. *Need to clear dataset from previous imputation procedure
. clear 

. cd "C:\Users\Jonathan A. Solis\Documents\Toshiba\Prosp\Killing_Msng\FPA\FPA_r
> eplication"
C:\Users\Jonathan A. Solis\Documents\Toshiba\Prosp\Killing_Msng\FPA\FPA_replica
> tion

. use Solis_fpa_may2019

. set more off

. 
. *Prepare dataset
. 
. *N. Log regime durability
. gen seq_ln=ln(seq1)
(527 missing values generated)

. 
. *create dummy years
. drop y1* y2*

. forvalues i=1992/2016{
  2. 
. gen y`i'=1 if year==`i'
  3. replace y`i'=0 if y`i'==.
  4. }
(4,086 missing values generated)
(4,086 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,085 missing values generated)
(4,085 real changes made)
(4,085 missing values generated)
(4,085 real changes made)

. *
. 
. *Setup for imputation
. mi set mlong

. 
. *Missingness table using model variables
. mi misstable patterns confirmed seq_ln polity public_cor physical_vd express_
> vd intensity2 info pop_ln hom_rate  

             Missing-value patterns
               (1 means complete)

              |   Pattern
    Percent   |  1  2  3  4    5  6  7  8    9
  ------------+--------------------------------
       48%    |  1  1  1  1    1  1  1  1    1
              |
       37     |  1  1  1  1    1  1  1  1    0
        8     |  1  0  0  0    0  0  0  0    0
        4     |  0  1  1  1    1  1  1  0    0
       <1     |  1  1  1  1    0  0  0  1    0
       <1     |  1  1  1  1    0  0  0  1    1
       <1     |  1  1  1  0    1  1  1  1    0
       <1     |  0  1  1  1    1  1  1  0    1
       <1     |  1  1  0  0    1  1  1  1    0
       <1     |  1  1  1  0    1  1  1  1    1
       <1     |  1  1  0  0    1  1  1  1    1
       <1     |  1  1  0  1    1  1  1  1    0
       <1     |  0  0  0  0    0  0  0  0    0
  ------------+--------------------------------
      100%    |

  Variables are  (1) polity2  (2) intensity2  (3) pop_ln  (4) info
                 (5) express_vd  (6) physical_vd  (7) public_cor  (8) seq_ln
                 (9) hom_rate

. 
. *Tell Stata which variables you're imputing and which you're not imputing
. 
. *NOT IMPUTING
. mi register regular confirmed seq_ln polity intensity2 durable2 pop_ln info e
> xpress_vd physical_vd public_cor ///
>   y199* y2*

. 
. *IMPUTING
. mi register imputed   hom_rate
(2148 m=0 obs. now marked as incomplete)

. 
. *Imputation mode: use all right-hand side variables to impute  hom_count. 20 
> diff. datasets
. mi impute mvn   hom_rate ///
>   = seq_ln polity intensity2  pop_ln info express_vd physical_vd public_cor, 
> add(20) rseed(623)  force

Performing EM optimization:
note: 1554 observations omitted from EM estimation because of all imputation
      variables missing
  observed log likelihood = -6092.9196 at iteration 1

Performing MCMC data augmentation ... 

Multivariate imputation                     Imputations =       20
Multivariate normal regression                    added =       20
Imputed: m=1 through m=20                       updated =        0

Prior: uniform                               Iterations =     2000
                                                burn-in =      100
                                                between =      100

------------------------------------------------------------------
                   |               Observations per m             
                   |----------------------------------------------
          Variable |   Complete   Incomplete   Imputed |     Total
-------------------+-----------------------------------+----------
          hom_rate |       2104         2148      1554 |      4252
------------------------------------------------------------------
(complete + incomplete = total; imputed is the minimum across m
 of the number of filled-in observations.)

Note: Right-hand-side variables (or weights) have missing values;
      model parameters estimated using listwise deletion.

. 
. *Summarize hom_count in different datasets (0= original, 1-20 are imputed dat
> asets)
. mi xeq 0 1 5 10 15 20: summarize hom_rate   

m=0 data:
-> summarize hom_rate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    hom_rate |      2,104    8.192548    12.95538          0   139.1321

m=1 data:
-> summarize hom_rate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    hom_rate |      3,658    9.243014    13.12044  -32.74342   139.1321

m=5 data:
-> summarize hom_rate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    hom_rate |      3,658    9.325918    13.06945  -29.21091   139.1321

m=10 data:
-> summarize hom_rate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    hom_rate |      3,658    9.495853    13.42284  -40.28541   139.1321

m=15 data:
-> summarize hom_rate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    hom_rate |      3,658    9.653282    13.11315  -37.87712   139.1321

m=20 data:
-> summarize hom_rate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    hom_rate |      3,658    9.682677    13.02117  -32.75779   139.1321

.  
. *****************************
. *Analysis using imputed data*
. *****************************
. 
. *Table 24: w/ homicide rates covariate (imputed)
. 
. *Model 1: Full (NBREG)
. mi estimate, post: nbreg confirmed seq_ln polity public_cor physical_vd expre
> ss_vd intensity2 info pop_ln hom_rate  ///
>   y199* y2* , cluster(ccode)  nolog 

Multiple-imputation estimates                   Imputations       =         20
Negative binomial regression                    Number of obs     =      3,586
                                                Average RVI       =     0.0281
                                                Largest FMI       =     0.2743
DF adjustment:   Large sample                   DF:     min       =     262.96
                                                        avg       = 1532685.43
                                                        max       =   1.20e+07
Model F test:       Equal FMI                   F(  31,765210.8)  =      20.56
Within VCE type:       Robust                   Prob > F          =     0.0000

                               (Within VCE adjusted for 160 clusters in ccode)
------------------------------------------------------------------------------
   confirmed |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.3313011   .0865931    -3.83   0.000    -.5010215   -.1615806
     polity2 |  -.0022263   .0271639    -0.08   0.935    -.0554672    .0510147
  public_cor |   .4817567   .5560556     0.87   0.386    -.6080941    1.571608
 physical_vd |  -4.559725   .7482514    -6.09   0.000     -6.02628    -3.09317
  express_vd |   3.467346   .7180663     4.83   0.000     2.059959    4.874733
  intensity2 |   1.336572   .1299776    10.28   0.000     1.081821    1.591324
        info |   .0296048   .0060116     4.92   0.000     .0178223    .0413872
      pop_ln |   .4069982   .0742331     5.48   0.000     .2615038    .5524926
    hom_rate |   .0109701   .0056267     1.95   0.052    -.0001091    .0220493
       y1992 |   .0026976   .4119667     0.01   0.995    -.8047434    .8101385
       y1993 |    .578388   .3821232     1.51   0.130    -.1705606    1.327337
       y1994 |   .6534203   .4025945     1.62   0.105    -.1356532    1.442494
       y1995 |   .1583911   .3485822     0.45   0.650    -.5248261    .8416084
       y1996 |  -.4606256   .4196734    -1.10   0.272    -1.283171    .3619197
       y1997 |  -.3513303   .4158905    -0.84   0.398    -1.166461    .4638005
       y1998 |  -.3096944   .3608421    -0.86   0.391    -1.016933    .3975438
       y1999 |   -.305694   .5046446    -0.61   0.545    -1.294785    .6833967
       y2000 |  -.3504782   .4141417    -0.85   0.397    -1.162182    .4612251
       y2001 |   .1259035   .4027959     0.31   0.755    -.6635623    .9153694
       y2002 |  -.7302345   .4154019    -1.76   0.079    -1.544407    .0839385
       y2003 |  -.1512154   .3474432    -0.44   0.663    -.8321921    .5297612
       y2004 |   .2252546   .3759946     0.60   0.549    -.5116818    .9621909
       y2005 |  -.1231795   .3793068    -0.32   0.745    -.8666077    .6202487
       y2006 |  -.6866946   .4029417    -1.70   0.088    -1.476446    .1030569
       y2007 |   -.055463   .3292137    -0.17   0.866    -.7007118    .5897857
       y2008 |  -.4879551   .3955497    -1.23   0.217    -1.263219    .2873086
       y2009 |   .0640964   .3941385     0.16   0.871    -.7084019    .8365947
       y2010 |   .0692839   .4054196     0.17   0.864    -.7253244    .8638921
       y2011 |  -.1118699    .306855    -0.36   0.715    -.7132969    .4895571
       y2012 |    .219211   .2641213     0.83   0.407    -.2984604    .7368825
       y2013 |   .1255098    .315218     0.40   0.691      -.49231    .7433295
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -9.966516   1.340827    -7.43   0.000     -12.5945   -7.338534
-------------+----------------------------------------------------------------
    /lnalpha |   .7602166   .2162396                      .3363947    1.184038
-------------+----------------------------------------------------------------
       alpha |   2.138739   .4624802                      1.399891    3.267544
------------------------------------------------------------------------------

. 
. *store estimates
. est sto rate_imp1

. 
. *Model 2: Auto (NBREG)
. mi estimate, post:  nbreg confirmed seq_ln polity public_cor physical_vd expr
> ess_vd intensity2 info pop_ln hom_rate  ///
>     y199* y2*   if durable2==0, cluster(ccode)

Multiple-imputation estimates                   Imputations       =         20
Negative binomial regression                    Number of obs     =        597
                                                Average RVI       =     1.7866
                                                Largest FMI       =     0.8734
DF adjustment:   Large sample                   DF:     min       =      25.46
                                                        avg       = 119,621.47
                                                        max       = 2265273.52
Model F test:       Equal FMI                   F(  31, 1595.3)   =      98.13
Within VCE type:       Robust                   Prob > F          =     0.0000

                                (Within VCE adjusted for 52 clusters in ccode)
------------------------------------------------------------------------------
   confirmed |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4225593   .2121497    -1.99   0.046    -.8383905   -.0067281
     polity2 |   .0504855   .2080334     0.24   0.808    -.3572645    .4582355
  public_cor |  -1.192561   1.173133    -1.02   0.309    -3.491875    1.106753
 physical_vd |  -3.644159   1.504173    -2.42   0.015    -6.592902    -.695416
  express_vd |   2.140972   1.795402     1.19   0.233    -1.378167     5.66011
  intensity2 |   2.053221   .3187069     6.44   0.000     1.428565    2.677877
        info |   .0390868   .0136237     2.87   0.004     .0123845    .0657891
      pop_ln |    .235875   .1413554     1.67   0.095    -.0412177    .5129676
    hom_rate |   .0008699   .0280483     0.03   0.975    -.0556653     .057405
       y1992 |  -.1370055   .9332394    -0.15   0.883    -1.966202    1.692191
       y1993 |   .4316496   .8848714     0.49   0.626    -1.302719    2.166018
       y1994 |   .7590887   .8081756     0.94   0.348    -.8258119    2.343989
       y1995 |  -.9743696   1.099651    -0.89   0.376    -3.129678    1.180939
       y1996 |  -1.165592   1.345151    -0.87   0.386    -3.802075    1.470892
       y1997 |  -.5185419   1.009084    -0.51   0.607    -2.496544     1.45946
       y1998 |  -1.348839   .9478979    -1.42   0.155    -3.206799     .509121
       y1999 |   .2345725   1.139552     0.21   0.837    -1.999111    2.468256
       y2000 |  -1.363577   1.452982    -0.94   0.348    -4.211423    1.484269
       y2001 |  -.5951443   1.668408    -0.36   0.721    -3.865192    2.674903
       y2002 |  -2.635742   .9673461    -2.72   0.006    -4.531908   -.7395759
       y2003 |  -.4544407   1.262219    -0.36   0.719    -2.928391    2.019509
       y2004 |     -.6527   1.486741    -0.44   0.661    -3.566719    2.261319
       y2005 |  -.3062922   1.189312    -0.26   0.797    -2.637463    2.024878
       y2006 |   .3737716   1.136032     0.33   0.742    -1.852953    2.600496
       y2007 |   .6211007   1.342815     0.46   0.644    -2.010811    3.253013
       y2008 |  -19.73228   1.567912   -12.59   0.000    -22.95847   -16.50609
       y2009 |   .4903226   1.083338     0.45   0.651    -1.633072    2.613717
       y2010 |  -.5593564   1.635025    -0.34   0.732    -3.763987    2.645274
       y2011 |   .3917128   .8304941     0.47   0.637    -1.236273    2.019698
       y2012 |   .6288649   .3574855     1.76   0.080    -.0761582    1.333888
       y2013 |   .4308438   .3364577     1.28   0.205    -.2412281    1.102916
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -6.055223   2.944118    -2.06   0.040    -11.82799   -.2824588
-------------+----------------------------------------------------------------
    /lnalpha |   1.154358     .53663                       .102582    2.206134
-------------+----------------------------------------------------------------
       alpha |   3.171987   1.702183                      1.108028    9.080545
------------------------------------------------------------------------------

. 
. *store estimates
. est sto rate_imp2

. 
. *Model 3: Ano (NBREG)
. mi estimate, post: nbreg confirmed seq_ln polity public_cor physical_vd expre
> ss_vd intensity2 info pop_ln hom_rate ///
>   y199* y2* if durable2==1, cluster(ccode)  nolog

Multiple-imputation estimates                   Imputations       =         20
Negative binomial regression                    Number of obs     =      1,067
                                                Average RVI       =     0.0450
                                                Largest FMI       =     0.4948
DF adjustment:   Large sample                   DF:     min       =      81.60
                                                        avg       = 4910562.80
                                                        max       =   1.79e+07
Model F test:       Equal FMI                   F(  31,302696.2)  =      13.50
Within VCE type:       Robust                   Prob > F          =     0.0000

                                (Within VCE adjusted for 86 clusters in ccode)
------------------------------------------------------------------------------
   confirmed |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.5876256   .1392744    -4.22   0.000    -.8605984   -.3146527
     polity2 |   .0338216   .0388276     0.87   0.384    -.0422795    .1099228
  public_cor |   .3651571   .7367462     0.50   0.620    -1.078844    1.809158
 physical_vd |  -4.283652   1.071837    -4.00   0.000    -6.384415    -2.18289
  express_vd |   3.771389   .8835126     4.27   0.000     2.039736    5.503042
  intensity2 |   1.243038   .1535945     8.09   0.000     .9419985    1.544078
        info |   .0307401   .0079817     3.85   0.000     .0150962    .0463841
      pop_ln |   .2537325   .1046495     2.42   0.015     .0486228    .4588422
    hom_rate |  -.0018741   .0108311    -0.17   0.863    -.0234221     .019674
       y1992 |   .1963029   .5547275     0.35   0.723    -.8909473    1.283553
       y1993 |   .8473603   .4366073     1.94   0.052    -.0083749    1.703096
       y1994 |   1.063893   .4805054     2.21   0.027     .1221168    2.005669
       y1995 |   .9326023   .4629235     2.01   0.044     .0252877    1.839917
       y1996 |   .0968391   .5824167     0.17   0.868    -1.044677    1.238356
       y1997 |  -1.336901   .6159919    -2.17   0.030    -2.544227   -.1295753
       y1998 |   .0150017   .5808482     0.03   0.979     -1.12344    1.153444
       y1999 |   .2267138   .5748833     0.39   0.693    -.9000372    1.353465
       y2000 |   .1263103   .5707908     0.22   0.825    -.9924193     1.24504
       y2001 |  -.2555745   .7910708    -0.32   0.747    -1.806045    1.294896
       y2002 |  -.9784036   .7029475    -1.39   0.164    -2.356156    .3993485
       y2003 |  -.0265026   .5691817    -0.05   0.963    -1.142078    1.089073
       y2004 |    .248477   .6841619     0.36   0.716    -1.092456     1.58941
       y2005 |   .6430816   .5629104     1.14   0.253    -.4602026    1.746366
       y2006 |   -.505059   .5494466    -0.92   0.358    -1.581955    .5718375
       y2007 |   .2759116   .5229228     0.53   0.598    -.7489988    1.300822
       y2008 |   .0082036   .5181539     0.02   0.987     -1.00736    1.023767
       y2009 |   .3680198   .5039634     0.73   0.465    -.6197308     1.35577
       y2010 |   .6476523   .6038363     1.07   0.283    -.5358451     1.83115
       y2011 |   .2067459   .3917408     0.53   0.598    -.5610544    .9745463
       y2012 |   .1919116    .472039     0.41   0.684    -.7332681    1.117091
       y2013 |   .8075934   .4822242     1.67   0.094    -.1375497    1.752736
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -7.500728   1.524425    -4.92   0.000     -10.4886   -4.512852
-------------+----------------------------------------------------------------
    /lnalpha |   .4902199   .2736317                     -.0460886    1.026528
-------------+----------------------------------------------------------------
       alpha |   1.632675   .4467516                      .9549574    2.791358
------------------------------------------------------------------------------

. 
. *store estimates
. est sto rate_imp3

. 
. *Model 4: Demo (NBREG)
. mi estimate, post:  nbreg confirmed seq_ln polity public_cor physical_vd expr
> ess_vd intensity2 info pop_ln hom_rate  ///
>   y199* y2* if durable2==2 & inrange(year,1995,2014), cluster(ccode)  nolog

Multiple-imputation estimates                   Imputations       =         20
Negative binomial regression                    Number of obs     =      1,699
                                                Average RVI       =     0.0223
                                                Largest FMI       =     0.0515
DF adjustment:   Large sample                   DF:     min       =   7,230.14
                                                        avg       =   3.77e+07
                                                        max       =   8.03e+08
Model F test:       Equal FMI                   F(  28, 1.2e+06)  =      17.37
Within VCE type:       Robust                   Prob > F          =     0.0000

                               (Within VCE adjusted for 103 clusters in ccode)
------------------------------------------------------------------------------
   confirmed |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.0280867   .1257649    -0.22   0.823    -.2745828    .2184093
     polity2 |   -.389139   .1645866    -2.36   0.018    -.7117228   -.0665552
  public_cor |   .5360102   .7233528     0.74   0.459    -.8817353    1.953756
 physical_vd |  -5.456373   .9341571    -5.84   0.000    -7.287293   -3.625453
  express_vd |   4.778617   1.440257     3.32   0.001     1.955764     7.60147
  intensity2 |   .6617489   .1632335     4.05   0.000     .3418169    .9816809
        info |   .0219324   .0064209     3.42   0.001     .0093477    .0345171
      pop_ln |   .5768199   .1066976     5.41   0.000     .3676965    .7859433
    hom_rate |   .0117873    .006489     1.82   0.069    -.0009331    .0245077
       y1992 |          0  (omitted)
       y1993 |          0  (omitted)
       y1994 |          0  (omitted)
       y1995 |   -.256193   .4816803    -0.53   0.595    -1.200274    .6878875
       y1996 |  -.4391247   .5932068    -0.74   0.459    -1.601792    .7235422
       y1997 |   .0801951   .5927967     0.14   0.892    -1.081665    1.242056
       y1998 |  -.4184968    .478696    -0.87   0.382    -1.356725    .5197315
       y1999 |  -1.241007   .6227283    -1.99   0.046    -2.461534   -.0204806
       y2000 |  -.3709273   .4912914    -0.76   0.450    -1.333841    .5919866
       y2001 |    .364778   .4768845     0.76   0.444    -.5698985    1.299455
       y2002 |  -.4218675   .4068106    -1.04   0.300    -1.219202    .3754674
       y2003 |  -.0805351    .445402    -0.18   0.857    -.9535074    .7924371
       y2004 |   .2653986   .4597895     0.58   0.564    -.6357722    1.166569
       y2005 |  -.4070667   .4942266    -0.82   0.410    -1.375733    .5615998
       y2006 |  -.5273123   .5034724    -1.05   0.295      -1.5141    .4594755
       y2007 |   .0063584   .3947459     0.02   0.987    -.7673294    .7800461
       y2008 |  -.1329575   .4975747    -0.27   0.789    -1.108186    .8422709
       y2009 |   .4146449   .5750575     0.72   0.471     -.712447    1.541737
       y2010 |  -.1289902   .5095903    -0.25   0.800    -1.127769    .8697886
       y2011 |  -.2646693   .4066625    -0.65   0.515    -1.061713    .5323746
       y2012 |  -.1883494   .3405637    -0.55   0.580    -.8558422    .4791433
       y2013 |  -.5185842    .465143    -1.11   0.265    -1.430248    .3930799
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -10.22376   2.673304    -3.82   0.000    -15.46333   -4.984176
-------------+----------------------------------------------------------------
    /lnalpha |   -.205827   .2488188                      -.693503     .281849
-------------+----------------------------------------------------------------
       alpha |   .8139739    .202532                      .4998221    1.325579
------------------------------------------------------------------------------

. 
. *store estimates
. est sto rate_imp4

. 
. ****Create TABLE 24 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab rate_imp1 rate_imp2 rate_imp3 rate_imp4 using apx_rate_imp.tex, replac
> e se aic obslast r2 ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" public_co
> r ///
> "Public Sect. Cor., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)" fix "CPJ Unconfirmed" ///
>  hom_rate "Homicides (rate)" ) ///
>  varwidth(2) scalar(N_g) drop(y1* y2*) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(note: file apx_rate_imp.tex not found)
(output written to apx_rate_imp.tex)

. 
.  *Table 25: w/ government compliance with judiciary covariate
.  
. *Need to clear dataset from previous imputation procedure
. clear 

. cd "C:\Users\Jonathan A. Solis\Documents\Toshiba\Prosp\Killing_Msng\FPA\FPA_r
> eplication"
C:\Users\Jonathan A. Solis\Documents\Toshiba\Prosp\Killing_Msng\FPA\FPA_replica
> tion

. use Solis_fpa_may2019

. set more off

. 
. *N. Log regime durability
. gen seq_ln=ln(seq1)
(527 missing values generated)

. 
. *create dummy years
. drop y1* y2*

. forvalues i=1992/2016{
  2. 
. gen y`i'=1 if year==`i'
  3. replace y`i'=0 if y`i'==.
  4. }
(4,086 missing values generated)
(4,086 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,084 missing values generated)
(4,084 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,083 missing values generated)
(4,083 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,082 missing values generated)
(4,082 real changes made)
(4,085 missing values generated)
(4,085 real changes made)
(4,085 missing values generated)
(4,085 real changes made)

. *
. 
. *Model 1: Global (NBREG)
. nbreg confirmed seq_ln polity comp_o physical_vd express_vd intensity2 info p
> op_ln ///
>    y199* y2*, cluster(ccode) nolog 
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      3,486
                                                Wald chi2(30)     =     533.58
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -1322.3759               Pseudo R2         =     0.2259

                                (Std. Err. adjusted for 155 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   -.321654   .0754105    -4.27   0.000    -.4694559   -.1738521
     polity2 |   .0178777   .0300415     0.60   0.552    -.0410026     .076758
      comp_o |     .03992   .1257444     0.32   0.751    -.2065345    .2863745
 physical_vd |  -5.266581   .7437031    -7.08   0.000    -6.724212   -3.808949
  express_vd |   3.333443   .8284754     4.02   0.000     1.709661    4.957225
  intensity2 |   1.275803   .1294808     9.85   0.000     1.022025    1.529581
        info |   .0261967   .0060653     4.32   0.000     .0143089    .0380845
      pop_ln |   .4011031   .0714685     5.61   0.000     .2610274    .5411787
       y1992 |  -.0802151   .4184266    -0.19   0.848    -.9003162    .7398861
       y1993 |    .476484   .4001019     1.19   0.234    -.3077013    1.260669
       y1994 |   .4871612    .411895     1.18   0.237    -.3201382    1.294461
       y1995 |   .0858908   .3676102     0.23   0.815    -.6346119    .8063935
       y1996 |  -.5213439   .4274601    -1.22   0.223     -1.35915    .3164625
       y1997 |  -.4590831   .4375894    -1.05   0.294    -1.316743    .3985763
       y1998 |  -.3302639   .3822564    -0.86   0.388    -1.079473    .4189449
       y1999 |  -.6355272    .521426    -1.22   0.223    -1.657503    .3864491
       y2000 |  -.4224872   .4279665    -0.99   0.324    -1.261286    .4163118
       y2001 |   -.031557   .4016474    -0.08   0.937    -.8187713    .7556574
       y2002 |   -.861941   .4412482    -1.95   0.051    -1.726771    .0028895
       y2003 |  -.1842211   .3762354    -0.49   0.624    -.9216289    .5531866
       y2004 |   .1075127   .3819381     0.28   0.778    -.6410721    .8560976
       y2005 |  -.3064932   .3953266    -0.78   0.438    -1.081319    .4683327
       y2006 |   -.728899   .4300505    -1.69   0.090    -1.571783    .1139845
       y2007 |  -.0773786   .3470095    -0.22   0.824    -.7575048    .6027475
       y2008 |  -.5895381   .4164557    -1.42   0.157    -1.405776    .2267001
       y2009 |   .0474925   .4097196     0.12   0.908    -.7555432    .8505283
       y2010 |   .0618475   .4313495     0.14   0.886    -.7835819     .907277
       y2011 |  -.1581763    .324337    -0.49   0.626    -.7938651    .4775126
       y2012 |   .1981724   .2664618     0.74   0.457    -.3240831    .7204278
       y2013 |   .0913438   .3095158     0.30   0.768    -.5152961    .6979837
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -8.881356   1.322221    -6.72   0.000    -11.47286    -6.28985
-------------+----------------------------------------------------------------
    /lnalpha |   .8143242   .2084858                      .4056995    1.222949
-------------+----------------------------------------------------------------
       alpha |   2.257649   .4706879                      1.500352    3.397191
------------------------------------------------------------------------------

. 
. *store estimates
. est sto comp1

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       comp1 |      3,486 -1708.337  -1322.376      32    2708.752    2905.76
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=2708.752 
. 
. *Model 2: Auto (NBREG)
. nbreg confirmed seq_ln polity comp_o physical_vd express_vd intensity2 info p
> op_ln ///
>    y199* y2* if durable2==0, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =        587
                                                Wald chi2(30)     =   25940.75
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -168.58401               Pseudo R2         =     0.2677

                                 (Std. Err. adjusted for 50 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |  -.4984626   .2345221    -2.13   0.034    -.9581174   -.0388078
     polity2 |   .0619193   .2016949     0.31   0.759    -.3333955    .4572341
      comp_o |   .3002736   .2462352     1.22   0.223    -.1823385    .7828857
 physical_vd |  -2.925379   1.173731    -2.49   0.013    -5.225849    -.624909
  express_vd |   .3019714   2.090047     0.14   0.885    -3.794446    4.398389
  intensity2 |   2.009134   .3215313     6.25   0.000     1.378944    2.639324
        info |   .0326649   .0155439     2.10   0.036     .0021993    .0631305
      pop_ln |   .2314927   .1415262     1.64   0.102    -.0458936    .5088791
       y1992 |   -.582953   1.045575    -0.56   0.577    -2.632242    1.466336
       y1993 |   .1197966   1.041336     0.12   0.908    -1.921184    2.160778
       y1994 |   .4031382   .9750504     0.41   0.679    -1.507926    2.314202
       y1995 |  -1.385379   1.169468    -1.18   0.236    -3.677494    .9067348
       y1996 |  -1.683067   1.353793    -1.24   0.214    -4.336452    .9703187
       y1997 |  -.9683881   1.048053    -0.92   0.355    -3.022535    1.085759
       y1998 |  -1.505633   1.018402    -1.48   0.139    -3.501664    .4903991
       y1999 |  -.2840561   1.716142    -0.17   0.869    -3.647633    3.079521
       y2000 |  -24.06722   .9246072   -26.03   0.000    -25.87942   -22.25503
       y2001 |  -.7100493   1.737395    -0.41   0.683    -4.115281    2.695182
       y2002 |  -3.112799   1.103124    -2.82   0.005    -5.274883   -.9507159
       y2003 |  -.7569644   1.577326    -0.48   0.631    -3.848466    2.334537
       y2004 |  -1.001176   1.574348    -0.64   0.525    -4.086842     2.08449
       y2005 |  -.5421434   1.207203    -0.45   0.653    -2.908219    1.823932
       y2006 |   .1302205   1.112888     0.12   0.907    -2.051001    2.311442
       y2007 |   .3965974   1.348223     0.29   0.769     -2.24587    3.039065
       y2008 |  -24.03017   .5941493   -40.44   0.000    -25.19468   -22.86566
       y2009 |   .2967868   1.036924     0.29   0.775    -1.735546     2.32912
       y2010 |  -.6835104   1.693412    -0.40   0.686    -4.002537    2.635516
       y2011 |   .1736513   .8342426     0.21   0.835    -1.461434    1.808737
       y2012 |   .4315329   .3421864     1.26   0.207    -.2391402    1.102206
       y2013 |   .4243261   .0801068     5.30   0.000     .2673197    .5813325
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -6.029305   2.915908    -2.07   0.039    -11.74438   -.3142308
-------------+----------------------------------------------------------------
    /lnalpha |   1.114204   .5996818                     -.0611503    2.289559
-------------+----------------------------------------------------------------
       alpha |   3.047143   1.827316                      .9406818    9.870587
------------------------------------------------------------------------------

. 
. *store estimates  
. est sto comp2

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       comp2 |        587 -230.2127   -168.584      32     401.168   541.1688
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=401.168
. 
. *Model 3: Ano (NBREG)
. nbreg confirmed seq_ln polity comp_o physical_vd express_vd intensity2 info p
> op_ln ///
>    y199* y2* if durable2==1, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,035
                                                Wald chi2(30)     =     384.31
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -457.73758               Pseudo R2         =     0.2185

                                 (Std. Err. adjusted for 83 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   -.536517   .1480378    -3.62   0.000    -.8266658   -.2463681
     polity2 |   .0545044   .0390443     1.40   0.163    -.0220211    .1310299
      comp_o |    .084307   .1546456     0.55   0.586    -.2187928    .3874067
 physical_vd |  -4.307078   1.121739    -3.84   0.000    -6.505647   -2.108509
  express_vd |   3.304332   .9739498     3.39   0.001     1.395426    5.213239
  intensity2 |   1.239724   .1599376     7.75   0.000     .9262515    1.553196
        info |   .0310918   .0080888     3.84   0.000     .0152381    .0469455
      pop_ln |   .2291901   .1029268     2.23   0.026     .0274573    .4309228
       y1992 |   .3514935   .5736958     0.61   0.540    -.7729296    1.475917
       y1993 |   .9924981   .4514458     2.20   0.028     .1076807    1.877316
       y1994 |   1.227796   .4701295     2.61   0.009     .3063588    2.149233
       y1995 |   1.028947   .4823994     2.13   0.033     .0834621    1.974433
       y1996 |   .2113472    .599195     0.35   0.724    -.9630534    1.385748
       y1997 |  -1.242803   .6227207    -2.00   0.046    -2.463313   -.0222928
       y1998 |   .1926456   .6112149     0.32   0.753    -1.005314    1.390605
       y1999 |   .3348061   .6019266     0.56   0.578    -.8449483     1.51456
       y2000 |   .2222416   .5659111     0.39   0.695    -.8869239    1.331407
       y2001 |  -.1358809   .8222089    -0.17   0.869    -1.747381    1.475619
       y2002 |  -1.812646   1.065265    -1.70   0.089    -3.900527     .275235
       y2003 |  -.0042721   .6068571    -0.01   0.994     -1.19369    1.185146
       y2004 |   .3927859   .6859732     0.57   0.567    -.9516969    1.737269
       y2005 |   .6035961   .5927566     1.02   0.309    -.5581856    1.765378
       y2006 |  -.5829979   .6211074    -0.94   0.348    -1.800346    .6343501
       y2007 |    .450403   .5203918     0.87   0.387    -.5695463    1.470352
       y2008 |  -.0192514   .5527624    -0.03   0.972    -1.102646    1.064143
       y2009 |   .5661547   .4992296     1.13   0.257    -.4123175    1.544627
       y2010 |   .6630911   .6578508     1.01   0.313    -.6262728    1.952455
       y2011 |   .1704173   .4335172     0.39   0.694    -.6792609    1.020096
       y2012 |   .3743999   .4658836     0.80   0.422    -.5387152    1.287515
       y2013 |   .8807953   .4902006     1.80   0.072    -.0799802    1.841571
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |   -6.95983   1.728064    -4.03   0.000    -10.34677   -3.572887
-------------+----------------------------------------------------------------
    /lnalpha |   .5969195   .2607525                       .085854    1.107985
-------------+----------------------------------------------------------------
       alpha |   1.816514   .4736607                      1.089647     3.02825
------------------------------------------------------------------------------

. 
. *store estimates  
. est sto comp3

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       comp3 |      1,035 -585.7416  -457.7376      32    979.4752   1137.624
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=979.4752
. 
. *Model 4: Demo (NBREG)
. nbreg confirmed seq_ln polity comp_o physical_vd express_vd intensity2 info p
> op_ln ///
>    y199* y2* if durable2==2, cluster(ccode) nolog
note: y2014 omitted because of collinearity
note: y2015 omitted because of collinearity
note: y2016 omitted because of collinearity

Negative binomial regression                    Number of obs     =      1,864
                                                Wald chi2(30)     =     618.72
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -604.54828               Pseudo R2         =     0.3100

                                (Std. Err. adjusted for 102 clusters in ccode)
------------------------------------------------------------------------------
             |               Robust
   confirmed |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      seq_ln |   .0497917   .0895967     0.56   0.578    -.1258145     .225398
     polity2 |  -.2717658   .1250844    -2.17   0.030    -.5169268   -.0266048
      comp_o |  -.2765058   .1775012    -1.56   0.119    -.6244017    .0713902
 physical_vd |   -7.01606    .851991    -8.23   0.000    -8.685931   -5.346188
  express_vd |   4.757263   1.492227     3.19   0.001     1.832552    7.681974
  intensity2 |   .5794944   .1261083     4.60   0.000     .3323266    .8266622
        info |   .0199248   .0058993     3.38   0.001     .0083624    .0314872
      pop_ln |   .5474072   .0903999     6.06   0.000     .3702266    .7245879
       y1992 |   .0264141   .5809493     0.05   0.964    -1.112226    1.165054
       y1993 |   .3481289   .5675927     0.61   0.540    -.7643324     1.46059
       y1994 |  -.9081219   .5085515    -1.79   0.074    -1.904865    .0886208
       y1995 |  -.1552896   .5400311    -0.29   0.774    -1.213731    .9031519
       y1996 |  -.3521311   .6393781    -0.55   0.582    -1.605289    .9010269
       y1997 |   .1824714    .630455     0.29   0.772    -1.053198     1.41814
       y1998 |  -.3118546   .5041373    -0.62   0.536    -1.299946    .6762363
       y1999 |  -1.124035   .6548824    -1.72   0.086    -2.407581    .1595106
       y2000 |  -.3527688   .5256431    -0.67   0.502     -1.38301    .6774728
       y2001 |   .2199627   .4780954     0.46   0.645    -.7170871    1.157012
       y2002 |  -.4397528   .4103071    -1.07   0.284     -1.24394    .3644343
       y2003 |  -.0673947   .4662114    -0.14   0.885    -.9811523    .8463628
       y2004 |   .1243637   .4778734     0.26   0.795    -.8122509    1.060978
       y2005 |  -.5541931   .5068632    -1.09   0.274    -1.547627    .4392405
       y2006 |  -.5867977   .5254604    -1.12   0.264    -1.616681    .4430858
       y2007 |   -.025181   .4132017    -0.06   0.951    -.8350414    .7846793
       y2008 |  -.2013138   .5127876    -0.39   0.695    -1.206359    .8037314
       y2009 |   .3251869   .6097956     0.53   0.594    -.8699904    1.520364
       y2010 |   -.034836   .5517169    -0.06   0.950    -1.116181    1.046509
       y2011 |  -.2098228   .4555164    -0.46   0.645    -1.102619     .682973
       y2012 |  -.2116075   .3652355    -0.58   0.562    -.9274561     .504241
       y2013 |  -.6095646   .5197488    -1.17   0.241    -1.628254    .4091243
       y2014 |          0  (omitted)
       y2015 |          0  (omitted)
       y2016 |          0  (omitted)
       _cons |  -8.316842   2.013708    -4.13   0.000    -12.26364   -4.370046
-------------+----------------------------------------------------------------
    /lnalpha |  -.2362376   .2665913                     -.7587469    .2862718
-------------+----------------------------------------------------------------
       alpha |   .7895931   .2104986                      .4682528    1.331454
------------------------------------------------------------------------------

. 
. *store estimates  
. est sto comp4

. 
. *AIC
. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
       comp4 |      1,864 -876.1655  -604.5483      32    1273.097   1450.072
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. *AIC=1273.097
. 
. ****Create TABLE 25 (Apx) for LaTex (basic table; I make some changes by hand
>  once generated)****
. esttab comp1 comp2 comp3 comp4 using jud_comp.tex, replace se aic obslast r2 
> ///
> mtitle("Global" "Autocracy" "Anocracy" "Democracy"  ) ///
> coeflabel(seq_ln "Regime-type Duration (ln)" polity2 "Polity Level" comp_o //
> /
> "Court Compl., V-Dem" physical_vd "Physical Integrity, V-Dem"  ///
> express_vd "Freedom of Exp., V-Dem" intensity2 "Armed Conflict" ///
>  info "Information Flows" pop_ln "Population (ln)") ///
>  varwidth(2) scalar(N_g) drop(y1* y2* _cons) b(%9.3f) t(%9.3f) r2(%9.2f) nolz
(note: file jud_comp.tex not found)
(output written to jud_comp.tex)

. 
. *****************************************
. *****************************************
. ** SECTION G: COUNTRIES BY REGIME TYPE **
. *****************************************
. *****************************************
. 
. *Generate data for table 26
. 
. *Auto
. tab country if durable2==0 & inrange(year,1992,2014)

                                country |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                            Afghanistan |          5        0.75        0.75
                                Algeria |          3        0.45        1.19
                                Armenia |          2        0.30        1.49
                             Azerbaijan |         20        2.98        4.47
                                Bahrain |         22        3.28        7.75
                             Bangladesh |          2        0.30        8.05
                                Belarus |         19        2.83       10.88
                                 Bhutan |         13        1.94       12.82
                               Cambodia |          1        0.15       12.97
               Central African Republic |          1        0.15       13.11
                                  China |         23        3.43       16.54
                             Congo, Rep |          4        0.60       17.14
                          Cote D'Ivoire |          7        1.04       18.18
                                   Cuba |         23        3.43       21.61
                               Djibouti |          7        1.04       22.65
                                  Egypt |         13        1.94       24.59
                      Equatorial Guinea |         20        2.98       27.57
                                Eritrea |         22        3.28       30.85
                                 Gambia |          3        0.45       31.30
                          Guinea-Bissau |          2        0.30       31.59
                                  Haiti |          2        0.30       31.89
                              Indonesia |          6        0.89       32.79
                                   Iran |         16        2.38       35.17
                                   Iraq |         11        1.64       36.81
                             Kazakhstan |         13        1.94       38.75
                           Korea, North |         23        3.43       42.18
                                 Kuwait |         23        3.43       45.60
                                   Laos |         23        3.43       49.03
                                Lesotho |          1        0.15       49.18
                                  Libya |         19        2.83       52.01
                                 Malawi |          2        0.30       52.31
                             Mauritania |         13        1.94       54.25
                                Morocco |         19        2.83       57.08
                             Mozambique |          2        0.30       57.38
                                Myanmar |         19        2.83       60.21
                                  Nepal |          4        0.60       60.80
                                  Niger |          3        0.45       61.25
                                Nigeria |          5        0.75       62.00
                                   Oman |         23        3.43       65.42
                               Pakistan |          3        0.45       65.87
                                  Qatar |         23        3.43       69.30
                                 Rwanda |          8        1.19       70.49
                           Saudi Arabia |         23        3.43       73.92
                                 Serbia |          7        1.04       74.96
                           Sierra Leone |          4        0.60       75.56
                                  Sudan |         13        1.94       77.50
                              Swaziland |         23        3.43       80.92
                                  Syria |         23        3.43       84.35
                             Tajikistan |          5        0.75       85.10
                           Turkmenistan |         23        3.43       88.52
                                    UAE |         23        3.43       91.95
                                 Uganda |          1        0.15       92.10
                             Uzbekistan |         23        3.43       95.53
                                Vietnam |         23        3.43       98.96
                               Zimbabwe |          7        1.04      100.00
----------------------------------------+-----------------------------------
                                  Total |        671      100.00

. *Ano
. tab country if durable2==1 & inrange(year,1992,2014)

                                country |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                            Afghanistan |          5        0.47        0.47
                                Albania |         10        0.93        1.40
                                Algeria |         20        1.86        3.26
                                 Angola |         23        2.14        5.40
                                Armenia |         18        1.68        7.08
                             Azerbaijan |          3        0.28        7.36
                                Bahrain |          1        0.09        7.45
                             Bangladesh |          6        0.56        8.01
                                Belarus |          1        0.09        8.10
                                 Bhutan |         10        0.93        9.03
                                 Bosnia |          3        0.28        9.31
                           Burkina Faso |         23        2.14       11.45
                                Burundi |         13        1.21       12.66
                               Cambodia |         22        2.05       14.71
                               Cameroon |         23        2.14       16.85
               Central African Republic |         22        2.05       18.90
                                   Chad |         23        2.14       21.04
                                Comoros |         12        1.12       22.16
                              Congo, DR |         23        2.14       24.30
                             Congo, Rep |         19        1.77       26.07
                          Cote D'Ivoire |         16        1.49       27.56
                                Croatia |          8        0.74       28.31
                               Djibouti |         16        1.49       29.80
                     Dominican Republic |          2        0.19       29.98
                                Ecuador |          8        0.74       30.73
                                  Egypt |         10        0.93       31.66
                      Equatorial Guinea |          3        0.28       31.94
                               Ethiopia |         23        2.14       34.08
                                   Fiji |         20        1.86       35.94
                                  Gabon |         23        2.14       38.08
                                 Gambia |         18        1.68       39.76
                                Georgia |         12        1.12       40.88
                                  Ghana |          9        0.84       41.71
                              Guatemala |          4        0.37       42.09
                                 Guinea |         23        2.14       44.23
                          Guinea-Bissau |         13        1.21       45.44
                                  Haiti |         16        1.49       46.93
                              Indonesia |          1        0.09       47.02
                                   Iran |          7        0.65       47.67
                                   Iraq |          4        0.37       48.04
                                 Jordan |         23        2.14       50.19
                             Kazakhstan |         10        0.93       51.12
                                  Kenya |         10        0.93       52.05
                             Kyrgyzstan |         19        1.77       53.82
                                Lesotho |          3        0.28       54.10
                                Liberia |         14        1.30       55.40
                                  Libya |          4        0.37       55.77
                             Madagascar |          5        0.47       56.24
                                 Malawi |          3        0.28       56.52
                               Malaysia |         17        1.58       58.10
                                   Mali |          3        0.28       58.38
                             Mauritania |         10        0.93       59.31
                                 Mexico |          5        0.47       59.78
                                Moldova |          1        0.09       59.87
                                Morocco |          4        0.37       60.24
                             Mozambique |         21        1.96       62.20
                                Myanmar |          4        0.37       62.57
                                  Nepal |          7        0.65       63.22
                                  Niger |          7        0.65       63.87
                                Nigeria |         18        1.68       65.55
                               Pakistan |          8        0.74       66.29
                       Papua New Guinea |         23        2.14       68.44
                                   Peru |          9        0.84       69.27
                                Romania |          4        0.37       69.65
                                 Russia |         16        1.49       71.14
                                 Rwanda |         15        1.40       72.53
                                Senegal |          8        0.74       73.28
                                 Serbia |          1        0.09       73.37
                           Sierra Leone |         11        1.02       74.39
                              Singapore |         23        2.14       76.54
                        Solomon Islands |          3        0.28       76.82
                                Somalia |         22        2.05       78.86
                              Sri Lanka |         18        1.68       80.54
                                  Sudan |         10        0.93       81.47
                               Suriname |         23        2.14       83.61
                             Tajikistan |         18        1.68       85.29
                               Tanzania |         23        2.14       87.43
                               Thailand |          6        0.56       87.99
                                   Togo |         23        2.14       90.13
                                Tunisia |         21        1.96       92.09
                                 Turkey |          1        0.09       92.18
                                 Uganda |         22        2.05       94.23
                                Ukraine |          2        0.19       94.41
                              Venezuela |          9        0.84       95.25
                                  Yemen |         23        2.14       97.39
                                 Zambia |         12        1.12       98.51
                               Zimbabwe |         16        1.49      100.00
----------------------------------------+-----------------------------------
                                  Total |      1,074      100.00

. *Demo
. tab country if durable2==2 & inrange(year,1992,2014)

                                country |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                Albania |         13        0.67        0.67
                              Argentina |         23        1.18        1.85
                                Armenia |          3        0.15        2.00
                              Australia |         23        1.18        3.19
                                Austria |         23        1.18        4.37
                             Bangladesh |         15        0.77        5.14
                                Belarus |          3        0.15        5.29
                                Belgium |         23        1.18        6.47
                                  Benin |         23        1.18        7.66
                                Bolivia |         23        1.18        8.84
                               Botswana |         23        1.18       10.02
                                 Brazil |         23        1.18       11.20
                               Bulgaria |         23        1.18       12.38
                                Burundi |         10        0.51       12.90
                                 Canada |         23        1.18       14.08
                             Cape Verde |         23        1.18       15.26
                                  Chile |         23        1.18       16.44
                               Colombia |         23        1.18       17.63
                                Comoros |         11        0.57       18.19
                             Costa Rica |         23        1.18       19.37
                                Croatia |         15        0.77       20.14
                                 Cyprus |         23        1.18       21.33
                         Czech Republic |         23        1.18       22.51
                                Denmark |         23        1.18       23.69
                     Dominican Republic |         21        1.08       24.77
                             East Timor |         13        0.67       25.44
                                Ecuador |         15        0.77       26.21
                            El Salvador |         23        1.18       27.39
                                Estonia |         23        1.18       28.57
                                   Fiji |          3        0.15       28.73
                                Finland |         23        1.18       29.91
                                 France |         23        1.18       31.09
                                 Gambia |          2        0.10       31.19
                                Georgia |         11        0.57       31.76
                                Germany |         23        1.18       32.94
                                  Ghana |         14        0.72       33.66
                                 Greece |         23        1.18       34.84
                              Guatemala |         19        0.98       35.82
                          Guinea-Bissau |          8        0.41       36.23
                                 Guyana |         23        1.18       37.41
                                  Haiti |          5        0.26       37.67
                               Honduras |         23        1.18       38.85
                                Hungary |         23        1.18       40.03
                                  India |         23        1.18       41.21
                              Indonesia |         16        0.82       42.03
                                   Iraq |          1        0.05       42.09
                                Ireland |         23        1.18       43.27
                                 Israel |         23        1.18       44.45
                                  Italy |         23        1.18       45.63
                                Jamaica |         23        1.18       46.81
                                  Japan |         23        1.18       48.00
                                  Kenya |         13        0.67       48.66
                           Korea, South |         23        1.18       49.85
                             Kyrgyzstan |          4        0.21       50.05
                                 Latvia |         23        1.18       51.23
                                Lebanon |         10        0.51       51.75
                                Lesotho |         19        0.98       52.72
                                Liberia |          9        0.46       53.19
                              Lithuania |         23        1.18       54.37
                             Luxembourg |         23        1.18       55.55
                              Macedonia |         23        1.18       56.73
                             Madagascar |         18        0.92       57.66
                                 Malawi |         18        0.92       58.58
                               Malaysia |          6        0.31       58.89
                                   Mali |         20        1.03       59.92
                              Mauritius |         23        1.18       61.10
                                 Mexico |         18        0.92       62.02
                                Moldova |         22        1.13       63.16
                               Mongolia |         23        1.18       64.34
                             Montenegro |          9        0.46       64.80
                                Namibia |         23        1.18       65.98
                                  Nepal |         12        0.62       66.60
                            Netherlands |         23        1.18       67.78
                            New Zealand |         23        1.18       68.96
                              Nicaragua |         23        1.18       70.14
                                  Niger |         13        0.67       70.81
                                 Norway |         23        1.18       71.99
                               Pakistan |         12        0.62       72.61
                                 Panama |         23        1.18       73.79
                               Paraguay |         23        1.18       74.97
                                   Peru |         14        0.72       75.69
                            Philippines |         23        1.18       76.88
                                 Poland |         23        1.18       78.06
                               Portugal |         23        1.18       79.24
                                Romania |         19        0.98       80.22
                                 Russia |          7        0.36       80.58
                                Senegal |         15        0.77       81.35
                                 Serbia |         15        0.77       82.12
                           Sierra Leone |          8        0.41       82.53
                               Slovakia |         22        1.13       83.66
                               Slovenia |         23        1.18       84.84
                        Solomon Islands |         19        0.98       85.82
                           South Africa |         23        1.18       87.00
                                  Spain |         23        1.18       88.18
                              Sri Lanka |          5        0.26       88.44
                                 Sweden |         23        1.18       89.62
                            Switzerland |         23        1.18       90.80
                               Thailand |         17        0.87       91.68
                    Trinidad and Tobago |         23        1.18       92.86
                                Tunisia |          2        0.10       92.96
                                 Turkey |         22        1.13       94.09
                                Ukraine |         21        1.08       95.17
                         United Kingdom |         23        1.18       96.35
                          United States |         23        1.18       97.53
                                Uruguay |         23        1.18       98.72
                              Venezuela |         14        0.72       99.43
                                 Zambia |         11        0.57      100.00
----------------------------------------+-----------------------------------
                                  Total |      1,946      100.00

. 
